Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling

arXiv:2609.02663 · cs.CV, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Comments: 16 pages, 3 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Decoupling — 20× in this paper · explained in 2 episodes
• Evidence Decoupling Decoder — 13× in this paper
• Transformer — 6× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 4× in this paper · explained in 6 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Interpretability — 3× in this paper · explained in 15 episodes
• Modality Gap — 3× in this paper · explained in 4 episodes
• Multimodal Learning — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Epistemic Uncertainty — 2× in this paper · explained in 10 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Entanglement — 1× in this paper · explained in 4 episodes
• Guided attention — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Medical Image Computing and Computer Assisted Intervention — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation
• Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
• UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities
• Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation
• Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
• MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning
• Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models
• Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation
• Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning
• BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

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