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CAFNet applies two branches to extract the features of dermoscopy and clinical images, and a hyper-branch to refine and fuse these features at all stages of the network. Specifically, the hyper-branch is composed of multiple co-attention fusion (CAF) modules.
To address these two issues, we propose a co-attention fusion network (CAFNet), which uses two branches to extract the features of dermoscopy and clinical ...
Oct 23, 2024 · To address these two issues, we propose a co-attention fusion network (CAFNet), which uses two branches to extract the features of dermoscopy ...
May 27, 2024 · In this work, we propose a novel multimodal co-attention fusion network (MCFN) with online data augmentation (ODA) for cancer subtype classification.
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Oct 4, 2022 · We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones.
Oct 4, 2022 · Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion.
Jul 13, 2024 · It employs a light and simple network for clinical images and a heavier, more complex one for dermoscopy images, resulting in significant ...
Co-Attention Fusion Network for Multimodal Skin Cancer Diagnosis · Medicine, Computer Science. Pattern Recognition · 2023.
Sep 1, 2024 · Chen, “Co-attention fusion network for multimodal skin cancer diagnosis,” Pattern Recognition,. vol. 133, p. 108990, Jan. 2023, doi: 10.1016 ...
This review offers a thorough analysis of the developments in deep learning-based multimodal fusion for medical classification tasks.