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Title & Speakers	Event
Belgium dbt Meetup #10 (in-person) 2025-04-29 · 16:00 The 10th edition of the Belgium dbt Meetup will take place in Leuven on April 29th. dbt Meetups are networking events open to all folks working with data! Talks predominantly focus on community members' experience with dbt, however, you'll catch presentations on broader topics such as analytics engineering, data stacks, data ops, modeling, testing, and team structures. 🏠Venue Host & Catering Provider: Dataminded, Vismarkt 17, 3000 Leuven 🤝Organizers: Sam Debruyn & Sofia Iamskaia 🚆 Getting there: see this link To attend, please read the Health and Safety Policy and Terms of Participation: https://www.getdbt.com/legal/health-and-safety-policy Our venue has capacity limits, so please only RSVP if you intend to come. Reach out - send a message in #local-belgium on Slack - if you need to cancel last minute or change your RSVP status on the Meetup to "Not Going." 📝Agenda: 18h00: welcome with food & drinks 18h45: start presentations 20h00: networking & drinks 🗣️Presentations: to dbt or not to dbt dbt has become king in the world of data transformations, but is it here to stay? We’ll explore how dbt’s competitors are trying to distinguish themselves, and how dbt is responding to the competition. by Michiel De Muynck, Senior Data Engineer @ Dataminded Michiel did a PhD and postdoc in computer science at Ghent University. In 2022, Michiel joined Datamindeld, where he has both used dbt at clients as well as taught dbt courses for Dataminded Academy. dbt @ Immoscoop Presentation on how our data platform changed before and after the introduction of dbt. We’ll explore how dbt helped us manage our data warehouse and drive Immoscoop’s strategy, touching on model types, materializations, semantic layers, and more. by Brecht Van Buggenhout, Data Analyst @ Immoscoop Brecht studied in the United States for six years and earned a Master’s in Business Analytics from the University of Wisconsin–Madison. After graduating, he started as a Data Analyst at Immoscoop, where he experienced the complete transformation of their data platform from day one. He now serves as a Team Lead, overseeing all data-related activities. We are always looking for speakers! To submit a session for one of the next meetups, please use our Sessionize page. Are you in doubt if you're ready to give a talk? Check out dbt Labs's guide on how to deliver a fantastic presentation! ➡️ Join the dbt Slack community: https://www.getdbt.com/community/ 🤝 For the best Meetup experience, make sure to join the #local-belgium channel in dbt Slack (https://slack.getdbt.com/)! dbt is the standard in data transformation, used by over 40,000 organizations worldwide. Through the application of software engineering best practices like modularity, version control, testing, and documentation, dbt’s analytics engineering workflow helps teams work more efficiently to produce data the entire organization can trust. Learn more: https://www.getdbt.com/	Belgium dbt Meetup #10 (in-person)
JavaScript London in Collaboration with Checkatrade 2024-10-03 · 17:00 Hi JavaScript London, We're thrilled to host our third meet-up of the year 😃 We'll be gathering at their awesome King's Cross office. Expect some fantastic tech talks, 🍕 pizzas, and 🍻 drinks, plus plenty of networking with fellow tech enthusiasts. Don't miss out on a night of great conversations and new connections! IMPORTANT: YOU MUST REGISTER FOR BUILDING ENTRY, PLEASE GO TO https://www.javascriptlondon.com/ AND SELECT "REGISTER FOR NEXT EVENT" - We have added a much-requested new feature to the website that should see you get a pop-up confirmation once you have registered. Talks: 1st Talk - Suyash Joshi - Smart Home Automation with Node-RED, Raspberry Pi, and InfluxDB 2nd Talk - Paul Braganza - Unleashing the Power of Chrome DevTools: Master Debugging & Supercharge Web Performance. 3rd Talk - Richard Moss - LLM application architectures and observability with LangSmith Full agenda: 6:00 PM: Doors Open. Grab a pizza slice, sip a beer, and start chatting with other attendees. 6:30 PM: Welcome remarks by Sam and Richard. We'll set the stage for an enriching night of learning and fun. 6:35 PM – 7:00 PM: First talk 7:05 PM: Second talk 7:30 PM: Toilet break 7:40: Third talk 8:00 PM: Closing remarks and an optional pub visit. Directions to Checkatrade: Located a 5 minutes walk from King's Cross Station the address is: Focus Point, 21 Caledonian Road, London, N1 9GB Sponsor: Checkatrade Checkatrade is an online directory and review platform that connects consumers with vetted and approved tradespeople for various home improvement and repair services in the UK. It allows users to search for professionals in their area, view customer reviews, and ensure the quality and reliability of the tradespeople they hire. The platform aims to provide peace of mind to consumers by offering a trusted source for finding reputable service providers.	JavaScript London in Collaboration with Checkatrade
Visual AI in Healthcare 2024-09-19 · 15:30 Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event! When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern Register for the Zoom: https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/ Interpretable AI Models in Radiology AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection. About the Speaker Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning. Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine. About the Speakers Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications. Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good. Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks. About the Speaker Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Exploring Instance Imbalance in Medical Semantic Segmentation Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation. About the Speaker Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.	Visual AI in Healthcare
Visual AI in Healthcare 2024-09-19 · 15:30 Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event! When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern Register for the Zoom: https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/ Interpretable AI Models in Radiology AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection. About the Speaker Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning. Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine. About the Speakers Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications. Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good. Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks. About the Speaker Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Exploring Instance Imbalance in Medical Semantic Segmentation Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation. About the Speaker Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.	Visual AI in Healthcare
Visual AI in Healthcare 2024-09-19 · 15:30 Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event! When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern Register for the Zoom: https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/ Interpretable AI Models in Radiology AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection. About the Speaker Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning. Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine. About the Speakers Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications. Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good. Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks. About the Speaker Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Exploring Instance Imbalance in Medical Semantic Segmentation Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation. About the Speaker Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.	Visual AI in Healthcare
Visual AI in Healthcare 2024-09-19 · 15:30 Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event! When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern Register for the Zoom: https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/ Interpretable AI Models in Radiology AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection. About the Speaker Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning. Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine. About the Speakers Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications. Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good. Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks. About the Speaker Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Exploring Instance Imbalance in Medical Semantic Segmentation Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation. About the Speaker Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.	Visual AI in Healthcare

Belgium dbt Meetup #10 (in-person) 2025-04-29 · 16:00

The 10th edition of the Belgium dbt Meetup will take place in Leuven on April 29th.

dbt Meetups are networking events open to all folks working with data! Talks predominantly focus on community members' experience with dbt, however, you'll catch presentations on broader topics such as analytics engineering, data stacks, data ops, modeling, testing, and team structures.

🏠Venue Host & Catering Provider: Dataminded, Vismarkt 17, 3000 Leuven 🤝Organizers: Sam Debruyn & Sofia Iamskaia 🚆 Getting there: see this link

To attend, please read the Health and Safety Policy and Terms of Participation: https://www.getdbt.com/legal/health-and-safety-policy

Our venue has capacity limits, so please only RSVP if you intend to come. Reach out - send a message in #local-belgium on Slack - if you need to cancel last minute or change your RSVP status on the Meetup to "Not Going."

📝Agenda:

18h00: welcome with food & drinks
18h45: start presentations
20h00: networking & drinks

🗣️Presentations:

to dbt or not to dbt dbt has become king in the world of data transformations, but is it here to stay? We’ll explore how dbt’s competitors are trying to distinguish themselves, and how dbt is responding to the competition.

by Michiel De Muynck, Senior Data Engineer @ Dataminded Michiel did a PhD and postdoc in computer science at Ghent University. In 2022, Michiel joined Datamindeld, where he has both used dbt at clients as well as taught dbt courses for Dataminded Academy.

dbt @ Immoscoop Presentation on how our data platform changed before and after the introduction of dbt. We’ll explore how dbt helped us manage our data warehouse and drive Immoscoop’s strategy, touching on model types, materializations, semantic layers, and more.

by Brecht Van Buggenhout, Data Analyst @ Immoscoop Brecht studied in the United States for six years and earned a Master’s in Business Analytics from the University of Wisconsin–Madison. After graduating, he started as a Data Analyst at Immoscoop, where he experienced the complete transformation of their data platform from day one. He now serves as a Team Lead, overseeing all data-related activities.

We are always looking for speakers!

To submit a session for one of the next meetups, please use our Sessionize page. Are you in doubt if you're ready to give a talk? Check out dbt Labs's guide on how to deliver a fantastic presentation!

➡️ Join the dbt Slack community: https://www.getdbt.com/community/

🤝 For the best Meetup experience, make sure to join the #local-belgium channel in dbt Slack (https://slack.getdbt.com/)!

dbt is the standard in data transformation, used by over 40,000 organizations worldwide. Through the application of software engineering best practices like modularity, version control, testing, and documentation, dbt’s analytics engineering workflow helps teams work more efficiently to produce data the entire organization can trust.

Learn more: https://www.getdbt.com/

Belgium dbt Meetup #10 (in-person)

JavaScript London in Collaboration with Checkatrade 2024-10-03 · 17:00

Hi JavaScript London,

We're thrilled to host our third meet-up of the year 😃

We'll be gathering at their awesome King's Cross office.

Expect some fantastic tech talks, 🍕 pizzas, and 🍻 drinks, plus plenty of networking with fellow tech enthusiasts. Don't miss out on a night of great conversations and new connections!

IMPORTANT: YOU MUST REGISTER FOR BUILDING ENTRY, PLEASE GO TO https://www.javascriptlondon.com/ AND SELECT "REGISTER FOR NEXT EVENT" - We have added a much-requested new feature to the website that should see you get a pop-up confirmation once you have registered.

Talks: 1st Talk - Suyash Joshi - Smart Home Automation with Node-RED, Raspberry Pi, and InfluxDB

2nd Talk - Paul Braganza - Unleashing the Power of Chrome DevTools: Master Debugging & Supercharge Web Performance.

3rd Talk - Richard Moss - LLM application architectures and observability with LangSmith

Full agenda: 6:00 PM: Doors Open. Grab a pizza slice, sip a beer, and start chatting with other attendees.

6:30 PM: Welcome remarks by Sam and Richard. We'll set the stage for an enriching night of learning and fun.

6:35 PM – 7:00 PM: First talk

7:05 PM: Second talk

7:30 PM: Toilet break

7:40: Third talk

8:00 PM: Closing remarks and an optional pub visit.

Directions to Checkatrade: Located a 5 minutes walk from King's Cross Station the address is: Focus Point, 21 Caledonian Road, London, N1 9GB

Sponsor: Checkatrade

Checkatrade is an online directory and review platform that connects consumers with vetted and approved tradespeople for various home improvement and repair services in the UK. It allows users to search for professionals in their area, view customer reviews, and ensure the quality and reliability of the tradespeople they hire. The platform aims to provide peace of mind to consumers by offering a trusted source for finding reputable service providers.

JavaScript London in Collaboration with Checkatrade

Visual AI in Healthcare 2024-09-19 · 15:30

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Visual AI in Healthcare

Visual AI in Healthcare 2024-09-19 · 15:30

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Visual AI in Healthcare

Visual AI in Healthcare 2024-09-19 · 15:30

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Visual AI in Healthcare

Visual AI in Healthcare 2024-09-19 · 15:30

Are you working at the intersection of visual AI and healthcare? Don’t miss this virtual event!

When: Sept 19, 2024 – 8:30 AM Pacific / 11:30 AM Eastern

Register for the Zoom:

https://voxel51.com/computer-vision-events/visual-ai-in-healthcare-sept-19-2024/

Interpretable AI Models in Radiology

AI methods have reached and even surpassed human-level accuracy in numerous areas of healthcare. However, adoption of these technologies into clinical workflows, where interpretability is of paramount importance, is slower compared to other industries. In this talk, we will present an overview of our research in improving the interpretability of AI models in medical image analysis through counterfactual examples and radiologist gaze data collection.

About the Speaker

Dr. Tasdizen is a Professor in Electrical and Computer Engineering and the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His areas of expertise are medical image analysis and machine learning.

Bridging Species with Pixels: Advancing Comparative Computational AI in Veterinary Oncology

Roughly 50% of dogs over the age of 10 years will develop cancer. Animals are now part of the family, and veterinary medical care now approximates what is available in humans. We are now at a pivotal time where AI platforms and products can expedite clinical discovery and decision - making and accelerate innovation. In this talk, we will provide a high-level overview of comparative AI and the work our team has initiated to evaluate both radiomic and language-based models in veterinary medicine.

About the Speakers

Dr. Christopher Pinard, DVM DVSc DACVIM (Oncology) is the CEO and co-founder of ANI.ML Health Inc., an adjunct professor in the Department of Clinical Studies at the Ontario Veterinary College, University of Guelph, a Medical Oncologist at Lakeshore Animal Health Partners, a Research Fellow at Sunnybrook Research Institute, and a Faculty Affiliate with the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) at the University of Guelph. His research focuses on comparative computational oncology and the development of computer vision and language model-based tools for clinical applications.

Dr. Kuan-Chuen Wu builds A.I. products and Engineering solutions via scientific research, technological development, and global teaching. With a Harvard-Stanford education in multi-disciplinary engineering, data science, and business management, he leads multi-functional teams and communities in generative A.I. and predictive A.I. using hardware, software, theory plus ingenuity for societal good.

Deep-Dive: NVIDIA’s VISTA-3D and MedSAM-2 Medical Imaging Models

In this talk, we’ll explore two medical imaging models. First, we’ll dive into NVIDIA’s Versatile Imaging SegmenTation and Annotation (VISTA) model which combines semantic segmentation with interactivity, offering high accuracy and adaptability across diverse anatomical areas for medical imaging. Finally, we’ll explore MedSAM-2, an advanced segmentation model that utilizes Meta’s SAM 2 framework to address both 2D and 3D medical image segmentation tasks.

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.

Exploring Instance Imbalance in Medical Semantic Segmentation

Current benchmarks in Medical Semantic Segmentation either leave out imbalanced datasets or focus on class imbalance. However, the nature of semantic segmentation shows that it is construed towards the segmentation of objects without differentiating multiple instances within a single class. This leads to the problem of instance imbalance in semantic segmentation. This is quite concerning in the case of medical image segmentation where the size of instances is principal. This talk will focus on a new evaluation metric and analysis of losses particularly to understand instance imbalance in semantic segmentation.

About the Speaker

Soumya Snigdha Kundu is a Ph.D. student at King’s College London. His work is focused on Trustworthy Machine Learning (TML) and its application to Neuro-Oncology.

Visual AI in Healthcare

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