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most citedRaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation

3 citations · 3 across the 1 of their papers we have counts for

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cs.CV20263 cited

RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation

Heng Li, Haojin Li, Jianyu Chen +3

Deep learning models often encounter challenges in making accurate inferences when there are domain shifts between the source and target data. This issue is particularly pronounced…

cs.CV2025

RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection

Wenjun Hou, Yi Cheng, Kaishuai Xu +4

Large language models (LLMs) have demonstrated remarkable capabilities in various domains, including radiology report generation. Previous approaches have attempted to utilize mult…

cs.CV2025

Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

Luoying Hao, Yan Hu, Yang Yue +4

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works…

cs.CV2024

Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry

Wenjun Hou, Yi Cheng, Kaishuai Xu +3

Comprehensively understanding surgical scenes in Surgical Visual Question Answering (Surgical VQA) requires reasoning over multiple objects. Previous approaches address this task u…

cs.CV2024

ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation

Wenjun Hou, Yi Cheng, Kaishuai Xu +3

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports. In this paper, we emphasize anothe…