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.
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.
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
Abstract:
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.