SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

Endoscopic Submucosal Dissection (ESD) is a minimally invasive procedure initially designed for the treatment of early gastric cancer but is now widely used for various gastrointestinal lesions. Computer-assisted Surgery systems have played a crucial role in improving the precision and safety of ESD procedures, however, their effectiveness is limited by the accurate recognition of surgical phases. The intricate nature of ESD, with different lesion characteristics and tissue structures, presents challenges for real-time surgical phase recognition algorithms. Existing surgical phase recognition algorithms struggle to efficiently capture temporal contexts in video-based scenarios, leading to insufficient performance. To address these issues, we propose SPRMamba, a novel Mamba-based framework for ESD surgical phase recognition. SPRMamba leverages the strengths of Mamba for long-term temporal modeling while introducing the Scaled Residual TranMamba block to enhance the capture of fine-grained details, overcoming the limitations of traditional temporal models like Temporal Convolutional Networks and Transformers. Moreover, a Temporal Sample Strategy is introduced to accelerate the processing, which is essential for real-time phase recognition in clinical settings. Extensive testing on the ESD385 dataset and the cholecystectomy Cholec80 dataset demonstrates that SPRMamba surpasses existing state-of-the-art methods and exhibits greater robustness across various surgical phase recognition tasks.

Deep Multimodal Collaborative Learning for Polyp Re-Identification

Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and 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 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.