4 papers · 1 filter
Synthetic Volumetric Data Generation Enables Zero-Shot Generalization of Foundation Models in 3D Medical Image Segmentation
Satrajit Chakrabarty, Sourya Sengupta, Gopal Avinash +1
Foundation models such as Segment Anything Model 2 (SAM 2) exhibit strong generalization on natural images and videos but perform poorly on medical data due to differences in appea…
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Sourya Sengupta, Satrajit Chakrabarty, Keerthi Sravan Ravi +2
Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in textur…
SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging
Danielle L. Ferreira, Ahana Gangopadhyay, Hsi-Ming Chang +2
Accurate segmentation of anatomical structures in ultrasound (US) images, particularly small ones, is challenging due to noise and variability in imaging conditions (e.g., probe po…
Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping
Hongxu Yang, Najib Akram Aboobacker, Xiaomeng Dong +3
X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by…