Dear all,
Next week, we have the pleasure of having Prof. Leo Joskowicz give a talk in the colloquium.
The seminar will be held on Monday, February 5th, at 14:00. Location: C220.
The title, abstract and bio appear below.
Looking forward to seeing you, Sagie and Liat
*Title:* Three is better than two and better than one: simultaneous deep learning for lesion changes analysis in medical images.
*Abstract:* Monitoring cancer patients undergoing treatment requires the analysis by an expert clinician of radiological images acquired every few months. With improvements in treatments leading to longer patient lifespans and increased accessibility of scanners, longitudinal patient studies have increased in their number of CT and MRI scans, making their interpretation more challenging and time-consuming. While numerous AI and deep learning based methods have been developed for medical image analysis, they fail short of providing a viable solution for radiological follow-up.
In this talk, we present a novel fully automatic end-to-end pipeline for the comprehensive detection and segmentation of cancer lesions and the analysis of their evolution over time. The key novelties are: 1) SimU-Net, a simultaneous multi-channel 3D deep learning model trained on pairs of registered scans of each patient that identifies the lesions and their changes based on the lesion and healthy tissue appearance differences; 2) a model-based bipartite graph lesions matching method for the analysis of lesion changes at the lesion level; 3) a method for longitudinal analysis of consecutive scans of a patient based on SimU-Net. We demonstrate the method with experimental studies on liver and lung metastases in CT scans and brain metastases in MRI scans that establish state-of the-art results.
*Bio*: Leo Joskowicz has been a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel since 1995. He is the founder and director of the Computer-Aided Surgery and Medical Image Processing Laboratory (CASMIP Lab). Prof. Joskowicz is a Fellow of the IEEE, ASME, and MICCAI (Medical Image Processing and Computer Aided Intervention) Societies. He is the past President of the MICCAI Society, was the Secretary General of the International Society of Computer Aided Orthopaedic Surgery (CAOS International) and of the International Society for Computer Assisted Surgery (ISCAS). He is the recipient of the 2010 Maurice E. Muller Award for Excellence in Computer Assisted Surgery by CAOS International and the 2007 Kaye Innovation Award. He has published two books and over 300 technical works including conference and journal papers, book chapters, and editorials and has 14 issued patents. He is on the Editorial Boards of Medical Image Analysis, Int. J. of Computer Aided Surgery, and Computer Aided Surgery and has served on numerous related program committees.
Reminder, this is happening today.
On Mon, Jan 29, 2024 at 5:00 PM Sagie Benaim sagie.benaim@mail.huji.ac.il wrote:
Dear all,
Next week, we have the pleasure of having Prof. Leo Joskowicz give a talk in the colloquium.
The seminar will be held on Monday, February 5th, at 14:00. Location: C220.
The title, abstract and bio appear below.
Looking forward to seeing you, Sagie and Liat
*Title:* Three is better than two and better than one: simultaneous deep learning for lesion changes analysis in medical images.
*Abstract:* Monitoring cancer patients undergoing treatment requires the analysis by an expert clinician of radiological images acquired every few months. With improvements in treatments leading to longer patient lifespans and increased accessibility of scanners, longitudinal patient studies have increased in their number of CT and MRI scans, making their interpretation more challenging and time-consuming. While numerous AI and deep learning based methods have been developed for medical image analysis, they fail short of providing a viable solution for radiological follow-up.
In this talk, we present a novel fully automatic end-to-end pipeline for the comprehensive detection and segmentation of cancer lesions and the analysis of their evolution over time. The key novelties are: 1) SimU-Net, a simultaneous multi-channel 3D deep learning model trained on pairs of registered scans of each patient that identifies the lesions and their changes based on the lesion and healthy tissue appearance differences; 2) a model-based bipartite graph lesions matching method for the analysis of lesion changes at the lesion level; 3) a method for longitudinal analysis of consecutive scans of a patient based on SimU-Net. We demonstrate the method with experimental studies on liver and lung metastases in CT scans and brain metastases in MRI scans that establish state-of the-art results.
*Bio*: Leo Joskowicz has been a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel since 1995. He is the founder and director of the Computer-Aided Surgery and Medical Image Processing Laboratory (CASMIP Lab). Prof. Joskowicz is a Fellow of the IEEE, ASME, and MICCAI (Medical Image Processing and Computer Aided Intervention) Societies. He is the past President of the MICCAI Society, was the Secretary General of the International Society of Computer Aided Orthopaedic Surgery (CAOS International) and of the International Society for Computer Assisted Surgery (ISCAS). He is the recipient of the 2010 Maurice E. Muller Award for Excellence in Computer Assisted Surgery by CAOS International and the 2007 Kaye Innovation Award. He has published two books and over 300 technical works including conference and journal papers, book chapters, and editorials and has 14 issued patents. He is on the Editorial Boards of Medical Image Analysis, Int. J. of Computer Aided Surgery, and Computer Aided Surgery and has served on numerous related program committees.
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Sagie Benaim