All are welcome to attend BMI grand rounds talk by Dr. Le Lu on 04/14. 

Le Lu, Ph.D 
Executive Director, PAII Inc 
Johns Hopkins University
IEEE Fellow, MICCAI Board Member


Time: Wednesday, April 14, 2021 3:00 pm - 4:00 pm 

Zoom Meeting 
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09 
Meeting ID: 956 1719 7636 Passcode: 924293

Title: 
In Search of Effective and Reproducible Clinical Imaging Biomarkers for Population Health and Oncology Applications of Screening, Diagnosis and Prognosis

Bio: 
Le Lu received a PhD in 2007 from Johns Hopkins University. During his first six years at Siemens, he made significant contributions to the company's CT colonography and Lung CAD product lines. From 2013 to 2017, Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences department of the National Institutes of Health Clinical Center. He then went on to found Nvidia's medical image analysis group and he held the position of senior research manager until June 2018. Since then, he has been the Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA which has become one of the leading industrial research labs in medical imaging. He was the main technical leader for two of the most-impactful public radiology image dataset releases (NIH ChestXray14, NIH DeepLesion 2018). He won NIH Clinical Center Director Award in 2017, NIH Mentor of the year award in 2015, and won numerous best paper awards in MICCAI and RSNA from 2016 to 2020 (over 10000 citations). In 2021, He was elected into IEEE Fellow class cited for his contribution to machine learning for cancer detection and diagnosis, and MICCAI society board member (MICCAI-Industry Workgroup Chair). He is currently an Associate Editor for IEEE Trans. Pattern Analysis and Machine Intelligence and IEEE Signal Processing Letters. He has served as an Area Chair for recent MICCAI, AAAI, CVPR, WACV, ICIP and ICHI conferences for 14 times.

Abstract: 
This talk will first give an overall on the work of employing deep learning to permit novel clinical workflows in two population health tasks, namely using conventional ultrasound for liver steatosis screening and quantitative reporting; osteoporosis screening via conventional X-ray imaging and AI readers. These two tasks were generally considered as infeasible tasks for human readers, but as proved by our scientific and clinical studies and peer-reviewed publications, they are suitable for AI readers. AI can be a supplementary and useful tool to assist physicians for cheaper and more convenient/precision patient management. Next, the main part of this talk describes a roadmap on three key problems in pancreatic cancer imaging solution: early screening, precision differential diagnosis, and deep prognosis on patient survival prediction. (1) Based on a new self- learning framework, we train the pancreatic ductal adenocarcinoma (PDAC) segmentation model using a larger quantity of patients (≈1,000, four institutions), with a mix of annotated/unannotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. Our approach makes it technically feasible for robust large-scale PDAC screening from multi-institutional multi-phase partially-annotated CT scans. (2) We propose a holistic segmentation-mesh classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask. Our results are comparable to a multimodality clinical test that combines clinical, imaging, and molecular testing for clinical management of patients with cysts. (3) Accurate preoperative prognosis of resectable PDACs for personalized treatment is highly desired in clinical practice. We present a novel deep neural network for the survival prediction of resectable PDAC patients, 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE- ConvLSTM), to derive the tumor attenuation signatures from CE-CT imaging studies. Our framework can significantly improve the prediction performances upon existing state-of-the-art survival analysis methods. This deep tumor signature has evidently added values (as a predictive biomarker) to be combined with the existing clinical staging system.

More information can be found at:
https://bmi.stonybrookmedicine.edu/sites/default/files/Lu_le_04_14.pdf

Submit an abstract celebrating research, new discoveries and achievements in medicine and science!

We encourage faculty, nurse practitioners, post-doctoral fellows, fellows, residents, medical students, graduate students and undergraduate students to submit an abstract. Original research, case reports and case series are welcome.

Abstract submission deadline: FEBRUARY 7, 2025

For more details, visit here.

Abstract: As intelligent systems become more integrated into human environments, fostering trustworthy human-AI collaboration presents a pressing challenge. In this talk, I examine the interplay between an agent's performance and social dynamics in shaping trust in human-AI interactions. My approach combines testbed development, behavioral prototyping, and user study design to create controlled experimental setups that capture real-world interaction complexities, such as ambiguity, multi-agent dynamics, and conflicting goals.

I illustrate this with a recent VR study on multi-user interaction with an autonomous vehicle (AV). Moving beyond dyadic interactions, the study probes human perspectives from the roles of a pedestrian, driver, and AV passenger, all interacting with the AV simultaneously at an ambiguous all-way stop sign intersection. We compare interactions with efficient and prosocial AV behavior strategies, revealing diverging trust perceptions and preferences across user roles. These insights inform a broader research trajectory focused on balancing performance with social considerations in designing trustworthy human-AI collaborations.

Bio: JiHyun Jeong is a postdoctoral researcher at Cornell University working on human-computer interaction and human-robot interaction. Her research develops prototypes and methods to explore performance and social factors that influence collaboration and trust between humans and artificial agents. She holds a Ph.D. and MPS in Information Science from Cornell University, and a BSc in Computer Science and Engineering from Korea University. She is a recipient of an honorable mention for best paper at DIS.

Zoom: https://stonybrook.zoom.us/j/98738234619?pwd=djJFQXBWbkpmblZDT25zNlVMYWpCQT09

Meeting ID: 987 3823 4619
Passcode: 474618

CSE 600 Seminar Series | Fall 2025


Abstract: Large reasoning models have demonstrated capabilities to solve competition-level math problems, answer deep research questions, and address complex coding needs. Much of this progress has been enabled by scaling of data: pre-training data to learn vast knowledge, fine-tuning data to learn natural language reasoning, and RL environments to refine that reasoning. In this talk, I will describe the current LLM reasoning paradigm, its boundaries, and the future of LLM reasoning beyond scaling. First, I will describe the state of reasoning models and where I think scaling can lead to some additional (though perhaps limited) successes. I will then shift to discussing more fundamental issues with models that scale will not resolve in the next few years. I will touch on four current limitations: outdated knowledge, generator-validator gaps, limited creativity, and poor compositional generalization. In all cases, fundamental limitations of LLMs or of supervised learning in general make these problems challenging, inviting future study and novel solutions beyond scaling.

Bio: Greg Durrett is an associate professor in the Department of Computer Science and the Center for Data Science at New York University. His research is broadly in the areas of natural language processing and machine learning. Currently, his group's focus is on reasoning about knowledge in text, verifying correctness of generation methods, and studying how to make progress on problems that defy LLM scaling. He is a 2023 Sloan Research Fellow and a recipient of a 2022 NSF CAREER award. He has served in numerous roles for ACL conferences, recently as a member of the NAACL Board since 2024 and as Senior Area Chair for ACL 2025 and EMNLP 2025. He received his BS in Computer Science and Mathematics from MIT and his PhD in Computer Science from UC Berkeley, where he was advised by Dan Klein.
Abstract: Humans perceive the world around them by recognizing global patterns and structures such as object parts, branches, their spatial arrangement, and so on. Most deep learning models, however, take a fundamentally local approach. They process images pixel-by-pixel rather than focusing on structures as a whole. While these models indeed perform well on many tasks, the local (pixel-level) versus global (structure-level) disconnect makes them harder to interpret and control.

Topology, in a general sense, is a mathematical language for describing structure. It delineates how different parts of an image relate to one another, capturing both individual structures and their overall layout. Preserving topology enforces structural correctness and, by extension, semantic validity.

In this thesis, we investigate how topological constraints can be used to bridge the gap between local and global understanding. We use topology to inform the design of deep learning models that are explicitly structure-aware. Our thesis focuses on dense prediction tasks, which include image segmentation, uncertainty estimation, and generative modeling. First, we introduce a topological interaction module for semantic segmentation that encodes containment and exclusion constraints directly into the learning process. This preserves anatomical hierarchies and improves multi-class consistency. Next, since segmentation models can never be truly perfect, we address the need for reliable uncertainty estimation to identify error-prone regions. Unlike conventional pixel-wise uncertainty maps, which tend to be noisy and difficult to interpret, we propose reasoning at the level of structural units--branches and connections--which are more visually discernible and actionable. Finally, we leverage topology for generative modeling. We propose a topology-guided diffusion framework that can be controlled using structural attributes like object count and connectivity.

Together, these contributions establish a unified approach to topology-informed, structure-preserving dense prediction models. By integrating topological reasoning with deep networks, this thesis advances models that are not only accurate, but also structurally consistent, interpretable, and controllable. The results from this thesis have been published in ECCV, NeurIPS, and ICLR.

Speaker: Saumya Gupta

Location: New Computer Science (NCS) 120


Zoom: https://stonybrook.zoom.us/j/93643318604?pwd=kv8DagpbayzizivU29UCYItnlzlYRM.1&jst=2

What AI tools are available to help with the scholarly research process? Are they helpful? What do they do and is it worth the time and energy to try them out? Join librarian Christine Fena to explore and compare established and emerging AI research tools such as Elicit, Scite, Consensus, and Undermind. The workshop will not offer a lengthy tutorial on how to use any of these tools, but will provide a starting point to understanding what they are, what new ones are emerging, and how AI research assistants might bring changes to your search process. All are welcome!

Register for this Zoom workshop.

Please join us for the next CSE 600 Seminar this Friday, October 11th, at 2:30pm in New Computer Science 120 given by Assistant Professor Mohammad Javad Amiri. Abstract: Today's distributed transaction processing systems must deal with untrustworthy environments where multiple mutually distrustful entities communicate with each other, and maintain data on untrusted infrastructure. Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by distributed transaction processing systems to establish consensus on the order of transactions. However, the proliferation of different BFT protocols has made it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. Moreover, as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This talk presents our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real-time to changing fault scenarios and workloads.