Dequan Wang 王德泉
Dequan Wang 王德泉
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MedFMC: A Real-world Dataset and Benchmark for Foundation Model Adaptation in Medical Image Classification
MedFMC, our novel dataset of 22,349 images from diverse clinical tasks, aims to bolster the use of pre-trained foundation models in medical image classification—a sector facing data scarcity. The application of MedFMC underscores the potential of these models and highlights the effectiveness of various baseline methods in terms of accuracy and cost-efficiency.
Dequan Wang
,
Xiaosong Wang
,
Lilong Wang
,
Mengzhang Li
,
Qian Da
,
Xiaoqiang Liu
,
Xiangyu Gao
,
Jun Shen
,
Junjun He
,
Tian Shen
,
Qi Duan
,
Jie Zhao
,
Kang Li
,
Yu Qiao
,
Shaoting Zhang
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arXiv
nature
Text-guided Foundation Model Adaptation for Pathological Image Classification
Our method, Connect Image and Text Embeddings (CITE), enhances pathological image classification by integrating biomedical text knowledge. CITE proves its superiority, particularly in low-data scenarios, with the PatchGastric stomach tumor dataset. Therefore, CITE provides a novel approach to enhancing data-efficient pathological image analysis using in-domain text knowledge.
Yunkun Zhang
,
Jin Gao
,
Mu Zhou
,
Xiaosong Wang
,
Yu Qiao
,
Shaoting Zhang
,
Dequan Wang
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arXiv
Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption
Our method, Diffusion-Driven Adaptation (DDA), adapts test inputs to enhance model accuracy on shifted data. Utilizing a generative diffusion model with image guidance and classifier self-ensembling, DDA surpasses traditional model adaptation approaches in handling various types of data corruption, diverse data quantities, and dependencies, as confirmed by the ImageNet-C benchmark tests.
Jin Gao
,
Jialing Zhang
,
Xihui Liu
,
Trevor Darrell
,
Evan Shelhamer
,
Dequan Wang
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arXiv
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