4 citations · 4 across the 6 of their papers we have counts for
6 papers
On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Prompts
Soumitri Chattopadhyay, Basar Demir, Marc Niethammer
While 3D foundational models have shown promise for promptable segmentation of medical volumes, their robustness to imprecise prompts remains under-explored. In this work, we aim t…
NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
Dzung Dinh, Boqi Chen, Yunni Qu +2
In many critical domains, features are not freely available at inference time: each measurement may come with a cost of time, money, and risk. Longitudinal prediction further compl…
Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study
Soumitri Chattopadhyay, Basar Demir, Marc Niethammer
Domain shift, caused by variations in imaging modalities and acquisition protocols, limits model generalization in medical image segmentation. While foundation models (FMs) trained…
Downstream Analysis of Foundational Medical Vision Models for Disease Progression
Basar Demir, Soumitri Chattopadhyay, Thomas Hastings Greer +2
Medical vision foundational models are used for a wide variety of tasks, including medical image segmentation and registration. This work evaluates the ability of these models to p…
CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
Peng Xia, Ze Chen, Juanxi Tian +21
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the…
A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness
Boqi Chen, Junier Oliva, Marc Niethammer
Medical records often consist of different modalities, such as images, text, and tabular information. Integrating all modalities offers a holistic view of a patient's condition, wh…