IBIIS & AIMI Seminar: Industrial Applied Research in Healthcare and Federated Learning at NVIDIA @ ZOOM:
IBIIS & AIMI Seminar: Industrial Applied Research in Healthcare and Federated Learning at NVIDIA
Jun 21 @ 12:00 pm – 1:00 pm ZOOM:

Daguang Xu, PhD
Senior Research Manager
NVIDIA Healthcare

Title: Industrial Applied Research in Healthcare and Federated Learning at NVIDIA

Abstract: As the market leader in deep learning and parallel computing, NVIDIA is fully committed to advancing applied research in medical imaging. Our goal is to revolutionize the capabilities of medical doctors and radiologists by equipping them with powerful tools and applications based on deep learning. We firmly believe that the integration of deep learning and accelerated AI will have a profound impact on the life sciences, medicine, and the healthcare industry as a whole. To drive this transformative process, NVIDIA is actively democratizing deep learning through the provision of a comprehensive AI computing platform specifically designed for the healthcare community. These GPU-accelerated solutions not only promote collaboration but also prioritize the security of each institution’s information. By doing so, we are fostering a collective effort in harnessing the potential of deep learning to benefit healthcare.

During this talk, I will showcase remarkable research achievements accomplished by NVIDIA’s deep learning in medical imaging team. This includes breakthroughs in segmentation, self-supervised learning, federated learning, and other related areas. Additionally, I will provide insights into the exciting avenues of research that our team is currently exploring.

IBIIS & AIMI Seminar: Negar Golestani, PhD & Jean Benoit Delbrouck, PhD @ Li Ka Shing, LK120 - Zoom Details on IBIIS website
IBIIS & AIMI Seminar: Negar Golestani, PhD & Jean Benoit Delbrouck, PhD
Sep 27 @ 2:00 pm – 3:00 pm Li Ka Shing, LK120 - Zoom Details on IBIIS website

Negar Golestani, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University

Title: AI in Radiology-Pathology Fusion Towards Precise Breast Cancer Detection

Abstract: Breast cancer is a global public health concern with various treatment options based on tumor characteristics. Pathological examination of excised tissue after surgery provides important information for treatment decisions. This pathology processing involving the manual selection of representative sections for histological examination is time-consuming and subjective, which can lead to potential sampling errors. Accurately identifying residual tumors is a challenging task, which highlights the need for systematic or assisted methods. Radiology-pathology registration is essential for developing deep-learning algorithms to automate cancer detection on radiology images. However, aligning faxitron and histopathology images is difficult due to content and resolution differences, tissue deformation, artifacts, and imprecise correspondence. We propose a novel deep learning-based pipeline for affine registration of faxitron images (x-ray representations of macrosections of ex-vivo breast tissue) with their corresponding histopathology images. Our model combines convolutional neural networks (CNN) and vision transformers (ViT), capturing local and global information from the entire tissue macrosection and its segments. This integrated approach enables simultaneous registration and stitching of image segments, facilitating segment-to-macrosection registration through a puzzling-based mechanism. To overcome the limitations of multi-modal ground truth data, we train the model using synthetic mono-modal data in a weakly supervised manner. The trained model successfully performs multi-modal registration, outperforms existing baselines, including deep learning-based and iterative models, and is approximately 200 times faster than the iterative approach. The application of proposed registration method allows for the precise mapping of pathology labels onto radiology images, thereby establishing ground truth labels for training classification and detection models on radiological data. This work bridges the gap in current research and clinical workflow, offering potential improvements in efficiency and accuracy for breast cancer evaluation and streamlining pathology workflow.

Jean Benoit Delbrouck, PhD
Research Scientist
Department of Radiology
Stanford University

Title: Generating Accurate and Factually Correct Medical Text
Abstract: Generating factually correct medical text is of utmost importance due to several reasons. Firstly, patient safety is heavily dependent on accurate information as medical decisions are often made based on the information provided. Secondly, trust in AI as a reliable tool in the medical field is essential, and this trust can only be established by generating accurate and reliable medical text. Lastly, medical research also relies heavily on accurate information for meaningful results.

Recent studies have explored new approaches for generating medical text from images or findings, ranging from pretraining to Reinforcement Learning, and leveraging expert annotations. However, a potential game changer in the field is the integration of GPT models in pipelines for generating factually correct medical text for research or production purposes.

IBIIS & AIMI Seminar: Why AI Should Replace Radiologists @ ZOOM:
IBIIS & AIMI Seminar: Why AI Should Replace Radiologists
Nov 15 @ 9:00 am – 10:00 am ZOOM:

Bram van Ginneken, PhD
Professor of Medical Image Analysis
Chair of the Diagnostic Image Analysis Group
Radboud University Medical Center

Title: Why AI Should Replace Radiologists

In this talk, I will provide arguments for the thesis that nearly all diagnostic radiology could be performed by computers and that the notion that AI will not replace radiologists is only temporarily true. Some well-known and lesser-known examples of AI systems analyzing medical images with a stand-alone performance on par or beyond human experts will be presented. I will show that systems built by academia, in collaborative efforts, may even outperform commercially available systems. Next, I will sketch a way forward to implement automated diagnostic radiology and argue that  this is needed to keep healthcare affordable in societies wrestling with aging populations. Some pitfalls, like excessive demands for trials, will be discussed. The key to success is to convince radiologists to take the lead in this process. They need to collaborate with AI developers, but AI developers and the medical device industry should not lead this process. Radiologists should, in fact, stop training radiologists, and instead, start training machines.

IBIIS & AIMI Seminar - NCI Imaging Data Commons: Towards Transparency, Reproducibility, and Scalability in Imaging AI @ Clark Center S360 - Zoom Details on IBIIS website
IBIIS & AIMI Seminar – NCI Imaging Data Commons: Towards Transparency, Reproducibility, and Scalability in Imaging AI
Mar 20 @ 12:00 pm – 1:00 pm Clark Center S360 - Zoom Details on IBIIS website

Andrey Fedorov, PhD 
Associate Professor, Harvard Medical School
Lead Investigator, Brigham and Women’s Hospital

Title: NCI Imaging Data Commons:Towards Transparency, Reproducibility, and Scalability in Imaging AI

The remarkable advances of artificial intelligence (AI) technology are revolutionizing established approaches to the acquisition, interpretation, and analysis of biomedical imaging data. Development, validation, and continuous refinement of AI tools requires  easy access to large high-quality annotated datasets, which are both representative and diverse. The National Cancer Institute (NCI) Imaging Data Commons (IDC) hosts over 50 TB of diverse publicly available cancer image data spanning radiology and microscopy domains. By harmonizing all  data based on industry standards and colocalizing it with analysis and exploration resources, IDC aims to facilitate the development, validation, and clinical translation of AI tools and address the well-documented challenges of establishing reproducible and  transparent AI processing pipelines. Balanced use of established commercial products with open-source solutions, interconnected  by standard interfaces, provides value and performance, while preserving sufficient agility to address the evolving needs of the research community. Emphasis on the development of tools, use cases to demonstrate the utility of uniform data representation, and  cloud-based analysis aim to ease adoption and help define best practices. Integration with other data in the broader NCI Cancer Research Data Commons infrastructure opens opportunities for multiomics studies incorporating imaging data to further empower the research community to accelerate breakthroughs in cancer detection, diagnosis, and treatment. The presentation will discuss the recent developments in IDC, highlighting resources, demonstrations and examples that we hope can help you improve your everyday imaging research practices – both those that use public and internal datasets.

IBIIS & AIMI Seminar: Building Fair and Trustworthy AI for Healthcare @ Clark Center S360 - Zoom Details on IBIIS website
IBIIS & AIMI Seminar: Building Fair and Trustworthy AI for Healthcare
Apr 17 @ 12:00 pm – 1:00 pm Clark Center S360 - Zoom Details on IBIIS website

Roxana Daneshjou, MD, PhD
Assistant Professor, Biomedical Data Science & Dermatology
Assistant Director, Center of Excellence for Precision Heath & Pharmacogenomics
Director of Informatics, Stanford Skin Innovation and Interventional Research Group
Stanford University

Title: Building Fair and Trustworthy AI for Healthcare

Abstract: AI for healthcare has the potential to revolutionize how we practice medicine. However, to do this in a fair and trustworthy manner requires special attention to how AI models work and their potential biases. In this talk, I will cover the considerations for building AI systems that improve healthcare.