Deep Multimodal Collaborative Learning for Polyp Re-Identification

2024-08-12

arXiv.org

AI Summary

Colonoscopy polyp re-identification faces two key challenges: domain shift induced by cross-view and cross-device variations, and insufficient discriminative power of unimodal representations. To address these, we propose a Deep Multimodal Collaborative Learning (DMCL) framework, introducing a novel dynamic multimodal feature fusion strategy that jointly leverages heterogeneous modalities—including visual features and structured clinical text—in an end-to-end trainable architecture. This enables complementary modality enhancement and domain-adaptive representation learning. Our method significantly improves robustness and generalization for cross-domain polyp matching. Extensive experiments on standard benchmarks demonstrate consistent superiority over state-of-the-art unimodal ReID approaches, validating the efficacy of multimodal representation learning in medical image re-identification. The source code is publicly available.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Matching polyps across different colonoscopic views and cameras
• Overcoming domain gap in CNN models for medical imaging
• Integrating multimodal data to enhance polyp re-identification accuracy

Innovation

• Multimodal collaborative learning for polyp re-identification
• Dynamic visual-text feature fusion via end-to-end training
• Enhanced generalization in medical scenarios through multimodal knowledge