4 citations · 6 across the 10 of their papers we have counts for
3 papers · 1 filter
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…