The paper introduces GPF‑Net, a Gated Progressive Fusion Network designed for colonoscopic polyp re‑identification. It fuses multimodal features from multiple levels using fully connected gating mechanisms, allowing layer‑wise refinement of semantic information. Experiments on standard benchmarks show that this multimodal, progressive fusion approach outperforms state‑of‑the‑art unimodal ReID models, especially for small polyps where fine‑grained details are crucial.
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
arXiv:2511. 01143v2 Announce Type: replace-cross Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry.
CoMLP introduces a cooperatively-gated MLP module that fuses multimodal medical data—such as imaging modalities and clinical reports—without relying on computationally heavy cross-attention. The module uses regional and dilated MLP interactions to capture both local and global cross-modal dependencies, enabling fine-grained fusion at high spatial resolutions. Experiments on five segmentation benchmarks, covering 2D/3D images and diverse anatomical regions, show consistent improvements over state-of-the-art multi-modal and language-guided methods, highlighting the effectiveness of MLP-based interaction for medical image segmentation.
arXiv:2510.06113v2 Announce Type: replace Abstract: Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which red...
arXiv:2610.32282v1 Announce Type: new Abstract: Early detection of colorectal polyps is essential for preventing colorectal cancer, making accurate polyp segmentation a key component in computer-aide...
The paper introduces W2F, a visual‑semantic mutual‑reinforcing framework for multi‑modal image fusion (MMIF). It enhances feature complementarity with a segmentation‑guided fusion loss and an embedded information restoration prior, and employs interactive gated mixed attention (IGM‑Att) and progressive coupling attention (PC‑Att) to fuse local and global information. A collaborative mechanism with a feature search module and gradient correction further aligns visual and semantic fusion, leading to state‑of‑the‑art visual appeal and decision accuracy.
arXiv:2610.07694v1 Announce Type: new Abstract: The integrative analysis of histopathological Whole-Slide Images (WSIs) and transcriptomic profiles holds significant promise for cancer survival predi...
LEAF-Mamba is a new framework for whole slide image analysis that jointly adapts and fuses frozen pretrained features. It uses a reconstruction‑regularized adapter to learn compact, task‑specific representations and combines parallel Mamba and convolutional streams to capture both long‑range dependencies and local interactions, with a gate that modulates their joint output before slide‑level pooling. Experiments show that this approach improves diagnostic classification and survival prediction over strong multiple‑instance learning baselines, and component analyses confirm the benefits of the adaptation and fusion modules.
BiCLIP is a bidirectional multimodal framework that enhances medical image segmentation by allowing visual features to iteratively refine textual representations, improving semantic alignment. It incorporates an augmentation consistency objective to stabilize learning against perturbed inputs. Experiments on QaTa-COV19 and MosMedData+ show that BiCLIP outperforms state‑of‑the‑art image‑only and multimodal baselines, achieving strong performance even with only 1% labeled data and resisting common clinical artifacts such as motion blur and low‑dose CT noise.
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity.
SCOUT is a concept‑grounded multimodal transformer that generates whole‑slide pathology reports by integrating local histological patterns, whole‑slide context, and expert‑curated diagnostic concepts. It uses evolving visual representations and recursively updated slide‑ and concept‑conditioned representations, with separate attention pathways during decoding that are fused adaptively for each token. Evaluated on TCGA‑BRCA, HistAI, and REG‑2025, SCOUT outperformed existing methods, improving BLEU, METEOR, and ROUGE‑L scores and raising the Clinical Report Quality Score on REG‑2025.