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…
Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models
Nurislam Tursynbek, Hastings Greer, Basar Demir +1
Diffusion models, while trained for image generation, have emerged as powerful foundational feature extractors for downstream tasks. We find that off-the-shelf diffusion models, tr…
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
Basar Demir, Yikang Liu, Xiao Chen +5
Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are…
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…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Basar Demir, Lin Tian, Thomas Hastings Greer +7
Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast…