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20222025
most citedCREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping

2 citations · 4 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

Dual Semantic-Aware Network for Noise Suppressed Ultrasound Video Segmentation

Ling Zhou, Runtian Yuan, Yi Liu +3

Ultrasound imaging is a prevalent diagnostic tool known for its simplicity and non-invasiveness. However, its inherent characteristics often introduce substantial noise, posing con…

cs.CV2025

AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation

Qingqiu Li, Zihang Cui, Seongsu Bae +8

Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings. Recent advancements in Large Multimodal Models (LMMs) have enabled automated CXR in…

cs.CV2025

Text-Promptable Propagation for Referring Medical Image Sequence Segmentation

Runtian Yuan, Mohan Chen, Jilan Xu +6

Referring Medical Image Sequence Segmentation (Ref-MISS) is a novel and challenging task that aims to segment anatomical structures in medical image sequences (\emph{e.g.} endoscop…

cs.CV2023

Enhanced Knowledge Injection for Radiology Report Generation

Qingqiu Li, Jilan Xu, Runtian Yuan +5

Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anoma…

cs.CV2022★ 1 cited

Graph Classification via Discriminative Edge Feature Learning

Yang Yi, Xuequan Lu, Shang Gao +2

Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when a…

cs.CV2022★ 2 cited

CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping

Jilan Xu, Junlin Hou, Yuejie Zhang +5

Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a clas…