2 citations · 3 across the 4 of their papers we have counts for
4 papers
Towards Better Ultrasound Video Segmentation Foundation Model: An Empirical study on SAM2 Finetuning from Data Perspective
Xing Yao, Ahana Gangopadhyay, Hsi-Ming Chang +1
Ultrasound (US) video segmentation remains a challenging problem due to strong inter- and intra-dataset variability, motion artifacts, and limited annotated data. Although foundati…
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…
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples
Chen Liu, Xiaomeng Dong, Michael Potter +2
Focal Loss has reached incredible popularity as it uses a simple technique to identify and utilize hard examples to achieve better performance on classification. However, this meth…
FastEstimator: A Deep Learning Library for Fast Prototyping and Productization
Xiaomeng Dong, Junpyo Hong, Hsi-Ming Chang +10
As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI pr…