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.

Related papers

• MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks [50.98856172702256]
  We propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach. MIND transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student. We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images.
  arXiv  Detail & Related papers  (2025-02-03T08:50:00Z)
• MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching [54.740256498985026]
  Keypoint detection and description methods often struggle with multimodal data. We propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching.
  arXiv  Detail & Related papers  (2025-01-20T06:56:30Z)
• Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment [11.897888221717245]
  This paper proposes a novel CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment. Our model is simple to implement without using task-specific external knowledge, and thus can easily migrate to other multi-modal tasks.
  arXiv  Detail & Related papers  (2024-03-11T01:07:36Z)
• Multimodal Representation Learning by Alternating Unimodal Adaptation [73.15829571740866]
  We propose MLA (Multimodal Learning with Alternating Unimodal Adaptation) to overcome challenges where some modalities appear more dominant than others during multimodal learning. MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process. It captures cross-modal interactions through a shared head, which undergoes continuous optimization across different modalities. Experiments are conducted on five diverse datasets, encompassing scenarios with complete modalities and scenarios with missing modalities.
  arXiv  Detail & Related papers  (2023-11-17T18:57:40Z)
• HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data [10.774128925670183]
  This paper presents the Hybrid Early-fusion Attention Learning Network (HEALNet), a flexible multimodal fusion architecture. We conduct multimodal survival analysis on Whole Slide Images and Multi-omic data on four cancer datasets from The Cancer Genome Atlas (TCGA) HEALNet achieves state-of-the-art performance compared to other end-to-end trained fusion models.
  arXiv  Detail & Related papers  (2023-11-15T17:06:26Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification [2.78481408391119]
  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 simple but effective training method named Colo-ReID, which can help our model learn more general and discriminative knowledge.
  arXiv  Detail & Related papers  (2023-08-02T04:10:14Z)
• Learning Unseen Modality Interaction [54.23533023883659]
  Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved.
  arXiv  Detail & Related papers  (2023-06-22T10:53:10Z)
• Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis [143.55901940771568]
  We propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis. In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality. A multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality.
  arXiv  Detail & Related papers  (2020-02-11T08:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.