SCALPEL: Semantic Cross-modal Alignment via LLM-Powered Encoder Learning for Medical Vision-Language Representation

• URL: http://arxiv.org/abs/2607.26885v1
• Date: Wed, 29 Jul 2026 13:12:32 GMT
• Title: SCALPEL: Semantic Cross-modal Alignment via LLM-Powered Encoder Learning for Medical Vision-Language Representation
• Abstract summary: Vision-language pre-training serves as a cornerstone for medical multimodal representation learning.<n>We propose textbfSCALPEL, a textbfSemantic textbfCross-modal textbfAlignment framework via textbfLLM-textbfPowered textbfEncoder textbfLearning.
• Score: 5.487826115955642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language pre-training (VLP) serves as a cornerstone for medical multimodal representation learning. However, existing medical VLP frameworks are often constrained by the limited context windows and shallow representational capacities of lightweight text encoders when processing lengthy, terminology-dense clinical reports. While integrating medical large language models (LLMs) offers unprecedented clinical reasoning capabilities, it introduces three major bottlenecks: (i) the anisotropic representational collapse of generative LLMs under standard contrastive objectives, (ii) the prohibitive memory overhead of joint end-to-end training with large batch sizes, and (iii) the medical hallucinations induced by vanilla contrastive losses that ignore fine-grained anatomical laterality and negation modifiers. To address these challenges, we propose \textbf{SCALPEL}, a \textbf{S}emantic \textbf{C}ross-modal \textbf{A}lignment framework via \textbf{L}LM-\textbf{P}owered \textbf{E}ncoder \textbf{L}earning. First, Clinical Report Contrastive fine-tuning converts a generative LLM into an isotropic encoder via domain-specific clinical text adaptation. Second, an asymmetric alignment strategy leverages offline feature caching to enable efficient training. Critically, we formulate an Anatomy-Negation Aware Objective that explicitly penalizes mismatched image-text pairs involving laterality confusion or false negations. Extensive experiments across MIMIC-CXR, CheXpert, and IU X-Ray benchmarks demonstrate that SCALPEL achieves state-of-the-art performance in cross-modal retrieval, zero-shot disease classification and medical visual question answering.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs [53.697403481143404]
  Large Vision-Language Models (LVLMs) have achieved strong performance across medical imaging tasks.<n>But they remain prone to factual inconsistencies, poor visual grounding, and misalignment with clinically meaningful feedback.
  arXiv  Detail & Related papers  (2026-06-10T18:35:36Z)
• CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis [0.4551615447454768]
  Wireless capsule endoscopy (WCE) enables non-invasive visual assessment of the small bowel.<n>Existing learning-based approaches for WCE are predominantly vision-only, often confined to narrow pathology sets.<n>This study introduces CapCLIP, a domain-specific vision-language representation learning framework for WCE.
  arXiv  Detail & Related papers  (2026-05-08T21:14:56Z)
• Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification [59.24009931000134]
  Multi-View Synergistic Learning (MVSL) is a unified framework that addresses adaptation paradigms, representation granularity, and disease semantic relationships.<n>MVSL decouples the adaptation of visual and textual encoders to respect their distinct representational characteristics.<n>It further introduces multi-granularity contrastive learning to explicitly model both global image semantics and localized lesion-level evidence.<n>MVSL consistently outperforms state-of-the-art methods in few-shot and zero-shot classification settings.
  arXiv  Detail & Related papers  (2026-04-27T02:41:27Z)
• TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation [56.09179939570486]
  We propose TGC-Net, a CLIP-based framework focusing on parameter-efficient, task-specific adaptations.<n>TGC-Net achieves state-of-the-art performance with substantially fewer trainable parameters, including notable Dice gains on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-12-24T12:06:26Z)
• Anatomical Region-Guided Contrastive Decoding: A Plug-and-Play Strategy for Mitigating Hallucinations in Medical VLMs [20.507007953026346]
  Anatomical Region-Guided Contrastive Decoding (ARCD) is a plug-and-play strategy that mitigates hallucinations by providing targeted, region-specific guidance.<n>Our method is effective in improving regional understanding, reducing hallucinations, and enhancing overall diagnostic accuracy.
  arXiv  Detail & Related papers  (2025-12-19T03:11:20Z)
• Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning [0.3499870393443268]
  Low back pain affects millions worldwide, driving the need for robust diagnostic models.<n>We present LumbarCLIP, a novel framework that leverages contrastive language-image pretraining to align lumbar spine MRI scans with corresponding radiological descriptions.
  arXiv  Detail & Related papers  (2025-09-25T06:52:25Z)
• Exploring the Capabilities of LLM Encoders for Image-Text Retrieval in Chest X-rays [8.019362739504087]
  Vision-language pretraining has advanced image-text alignment, yet progress in radiology remains constrained by the heterogeneity of clinical reports.<n>We ask whether large language model (LLM) encoders can provide robust clinical representations that transfer across diverse styles.<n>We introduce LLM2VEC4CXR, a domain-adapted encoder for chest X-ray reports, and LLM2CLIP4CXR, a dual-tower framework that couples this encoder with a vision backbone.
  arXiv  Detail & Related papers  (2025-09-17T09:44:59Z)
• VELVET-Med: Vision and Efficient Language Pre-training for Volumetric Imaging Tasks in Medicine [11.993301266706139]
  We propose a vision-language pre-training framework, termed as textbfVELVET-Med, specifically designed for limited volumetric data such as 3D CT and associated radiology reports.<n>Our approach seeks to uncover rich spatial and semantic relationships embedded in volumetric medical images and corresponding clinical narratives.<n>The resulting encoders exhibit strong transferability, achieving state-of-the-art performance across a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2025-08-16T17:08:43Z)
• Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs [51.93737995405164]
  Large Vision-Language Models (LVLMs) are susceptible to hallucinations.<n>We introduce a novel Conditional Pointwise Mutual Information (C-PMI) calibrated decoding strategy.<n>We show that the proposed method significantly reduces hallucinations in LVLMs while preserving decoding efficiency.
  arXiv  Detail & Related papers  (2025-05-26T08:36:10Z)
• Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding [66.06337890279839]
  Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding for downstream multi-modal tasks.<n>LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content.<n>We propose an Inter-Modality Correlation Decoding (IMCCD) method to mitigate hallucinations in LVLMs in a training-free manner.
  arXiv  Detail & Related papers  (2025-01-03T17:56:28Z)
• Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding [92.32881381717594]
  We introduce ALternate Contrastive Decoding (ALCD) to solve hallucination issues in medical information extraction tasks. ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
  arXiv  Detail & Related papers  (2024-10-21T07:19:19Z)
• Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging [7.912408164613206]
  In clinical practice, segmenting specific lesions can significantly enhance diagnostic accuracy and treatment efficiency.<n>We propose a novel model, Language-guided Scale-aware MedSegmentor (LSMS), which segments target lesions in medical images based on given textual expressions.<n>Our LSMS consistently achieves superior performance with significantly lower computational cost.
  arXiv  Detail & Related papers  (2024-08-30T15:22:13Z)
• OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis [6.4136876268620115]
  Self-supervised learning (SSL) has emerged as a promising technique for analyzing medical modalities such as X-rays.<n>We propose OTCXR, a novel SSL framework that leverages optimal transport (OT) to learn dense semantic invariance.<n>We validate OTCXR's efficacy through comprehensive experiments on three publicly available chest X-ray datasets.
  arXiv  Detail & Related papers  (2024-04-18T02:59:48Z)

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.