activity
20182023
most citedSTU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

52 citations · 122 across the 14 of their papers we have counts for

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Showing 2023Show all

12 papers · 1 filter

cs.CL2023

Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies

Pengcheng Chen, Ziyan Huang, Zhongying Deng +6

OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its promise, recent studies a…

eess.IV202314 cited

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Jin Ye, Junlong Cheng, Jianpin Chen +12

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massiv…

cs.CV2023

SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Haoyu Wang, Sizheng Guo, Jin Ye +11

Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalit…

eess.IV2023

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Ziyan Huang, Zhongying Deng, Jin Ye +11

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recen…

cs.CV202326 cited

SAM-Med2D

Junlong Cheng, Jin Ye, Zhongying Deng +12

The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points an…

eess.IV20231 cited

Artifact Restoration in Histology Images with Diffusion Probabilistic Models

Zhenqi He, Junjun He, Jin Ye +1

Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both patholog…