most citedScale-Equivariant UNet for Histopathology Image Segmentation

2 citations · 2 across the 6 of their papers we have counts for

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cs.CV2026

GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting

Yilong Yang, Jianxin Tian, Shengchuan Zhang +1

Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by reference cues. While supervised methods are annotation-heavy and training-free approa…

cs.CV2026

Discover, Segment, and Select: A Progressive Mechanism for Zero-shot Camouflaged Object Segmentation

Yilong Yang, Jianxin Tian, Shengchuan Zhang +1

Current zero-shot Camouflaged Object Segmentation methods typically employ a two-stage pipeline (discover-then-segment): using MLLMs to obtain visual prompts, followed by SAM segme…

cs.CV2023

3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks

Marcel Beetz, Yilong Yang, Abhirup Banerjee +2

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. Howeve…

cs.CV2023

Rotation-Scale Equivariant Steerable Filters

Yilong Yang, Srinandan Dasmahapatra, Sasan Mahmoodi

Incorporating either rotation equivariance or scale equivariance into CNNs has proved to be effective in improving models' generalization performance. However, jointly integrating…

cs.CV2023★ 2 cited

Scale-Equivariant UNet for Histopathology Image Segmentation

Yilong Yang, Srinandan Dasmahapatra, Sasan Mahmoodi

Digital histopathology slides are scanned and viewed under different magnifications and stored as images at different resolutions. Convolutional Neural Networks (CNNs) trained on s…