activity
20222024
most citedSegment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

97 citations · 97 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Nucleus subtype classification using inter-modality learning

Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +14

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available b…

cs.CV2023

Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures

Shunxing Bao, Sichen Zhu, Vasantha L Kolachala +16

Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, part…

cs.CV2023

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Can Cui, Yaohong Wang, Shunxing Bao +11

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during train…

cs.CV2023

Exploring shared memory architectures for end-to-end gigapixel deep learning

Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios +8

Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development of new models has been the saturation of GPU…

eess.IV202397 cited

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Ruining Deng, Can Cui, Quan Liu +13

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed an…

cs.CV2022

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

Ruining Deng, Can Cui, Lucas W. Remedios +12

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotation…