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Seminar Series

February 19

Human-Machine Partnerships in Computer-Integrated Interventional Medicine: Yesterday, Today, and Tomorrow

Russell H. Taylor
Johns Hopkins University, Baltimore, MD

Part Of

11:00A
- 12:00P

Location

32-370
Add to Calendar 2026-02-19 11:00:00 2026-02-19 12:00:00 America/New_York Human-Machine Partnerships in Computer-Integrated Interventional Medicine: Yesterday, Today, and Tomorrow This talk will discuss insights gathered over 35 years of research on medical robotics and computer-integrated interventional medicine (CIIM), both at IBM and at Johns Hopkins University. The goal of this research has been the creation of a three-way partnership between physicians, technology, and information to improve treatment processes. CIIM systems combine innovative algorithms, robotic devices, imaging systems, sensors, and human-machine interfaces to work cooperatively with surgeons in the planning and execution of surgery and other interventionalprocedures. For individual patients, CIIM systems can enable less invasive, safer, and more cost-effective treatments. Since these systems have the ability to act as “flight data recorders” in the operating room, they can enable the use of statistical methods to improve treatment processes for future patients and to promote physician training. We will illustrate these themes with examples from our past and current work, with special attention to the human-machine partnership aspects, and will offer some thoughts about future research opportunities and system evolution. TBD

December 04

Deep Learning Radiology for Fetal Development Assessment in MRI

Leo Joskowicz and Dafna Ben Bashat
The Hebrew University of Jerusalem and Tel Aviv University, Israel

Part Of

11:00A
- 12:00P

Location

32-D451
Add to Calendar 2025-12-04 11:00:00 2025-12-04 12:00:00 America/New_York Deep Learning Radiology for Fetal Development Assessment in MRI Human fetal development is a complex process where the fetus changes in size, structure, and maturation occur in a unique spatio-temporal pattern. The early detection of developmental disorders relies on the evaluation of ultrasound (US) images and on a few manual measurements performed on them. Magnetic Resonance Imaging (MRI) is increasingly used to obtain additional and more accurate biomarkers when US results are inconclusive. However, manual acquisition of MRI-based biomarkers is time consuming, requires expertise and is prone to annotator variability.In this talk, we present a novel annotation methodology and deep learning methods for the automatic computation of standard and novel biometric measures in fetal MRI. Specifically, we describe: 1) a bootstrapping approach for optimizing the manual annotation and correction of fetal structures and for training deep learning models with very few annotated datasets; 2) pipelines for computing standard linear and volumetric biometric measures on MRI scans; 3) new ocular and fetal fat MRI-based biometric markers and methods to compute them. We demonstrate the clinical potential of our methods on fetal whole-body weight estimation and placenta and fetal brain structures segmentation. TBD

November 14

Physics Informed Deep Unfolded Full Waveform Inversion for Edema Detection

Yonatan Kvich
Weizmann Institute of Science, Israel

Part Of

11:00A
- 12:00P

Location

32-D407
Add to Calendar 2025-11-14 11:00:00 2025-11-14 12:00:00 America/New_York Physics Informed Deep Unfolded Full Waveform Inversion for Edema Detection Accurate detection of edema is clinically important but remains challenging due to the subtlety of its quantitative indicators. Ultrasound (US) offers a safe, accessible, and cost-effective imaging modality, yet conventional beamforming methods such as B-mode do not directly recover the tissue’s physical parameters. In this work, we present a physics-informed deep learning approach that performs inverse reconstruction of tissue properties directly from raw channel data, enabling quantitative estimation of the speed of sound and density. Our method, called Deep-Unfolded Full Waveform Inversion (DUFWI), unfolds the iterative steps of a classical inverse solver into a trainable neural network, preserving physical interpretability while learning efficient update rules from data. We demonstrate results on both simulated datasets and real hardware experiments using a Verasonics US system with phantom setups containing cylindrical rods of known speed of sound, showing substantial improvement over traditional FWI and MB-QRUS in performance and computational demand. The framework can be used for a wide range of inverse US imaging tasks, offering a practical path toward real-time, physics-based diagnostic imaging. TBD

November 05

Foundation of Prenatal Risk

Mads Nielsen
University of Copenhagen

Part Of

11:00A
- 12:00P

Location

32-370
Add to Calendar 2025-11-05 11:00:00 2025-11-05 12:00:00 America/New_York Foundation of Prenatal Risk Most pregnant women undertake at least two ultrasound exams as well as physical and biochemical examinations. Nevertheless, many adverse pregnancy outcomes are detected timely with only very low sensitivity. Preterm birth 40%, Low weight for gestational age 25%, congenital heart disease 40%. Maternal health history as well as ultrasound exams are obvious objects for deep learning-based early detection of adverse outcomes. We develop foundation models for mother's health history and for ultrasound exams and shows fine-tunings with substantial increase in sensitivity of several adverse outcomes within the time-window of intervention. Data are based on national Danish data from more than 700.000 pregnancies. TBD