Deep Multimodal Collaborative Learning for Polyp Re-Identification

• URL: http://arxiv.org/abs/2408.05914v2
• Date: Tue, 24 Sep 2024 14:35:17 GMT
• Title: Deep Multimodal Collaborative Learning for Polyp Re-Identification
• Authors: Suncheng Xiang, Jincheng Li, Zhengjie Zhang, Shilun Cai, Jiale Guan, Dahong Qian,
• Abstract summary: Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras. Traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset produce unsatisfactory retrieval performance. We propose a novel Deep Multimodal Collaborative Learning framework named DMCL for polyp re-identification.
• Score: 4.4028428688691905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras, which plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due to the large domain gap. Worsely, these solutions typically learn unimodal modal representations on the basis of visual samples, which fails to explore complementary information from other different modalities. To address this challenge, we propose a novel Deep Multimodal Collaborative Learning framework named DMCL for polyp re-identification, which can effectively encourage modality collaboration and reinforce generalization capability in medical scenarios. On the basis of it, a dynamic multimodal feature fusion strategy is introduced to leverage the optimized multimodal representations for multimodal fusion via end-to-end training. Experiments on the standard benchmarks show the benefits of the multimodal setting over state-of-the-art unimodal ReID models, especially when combined with the specialized multimodal fusion strategy, from which we have proved that learning representation with multiple-modality can be competitive to methods based on unimodal representation learning. We also hope that our method will shed light on some related researches to move forward, especially for multimodal collaborative learning. The code is publicly available at https://github.com/JeremyXSC/DMCL.

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