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20212025
most citedSegment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

97 citations · 108 across the 11 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

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.IV2023★ 97 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…

eess.IV2023

Cross-scale Multi-instance Learning for Pathological Image Diagnosis

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

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (M…