MedVision: Dataset and Benchmark for Quantitative Medical Image Analysis

• URL: http://arxiv.org/abs/2511.18676v1
• Date: Mon, 24 Nov 2025 01:26:07 GMT
• Title: MedVision: Dataset and Benchmark for Quantitative Medical Image Analysis
• Abstract summary: We introduce MedVision, a dataset and benchmark designed to evaluate and improve vision-language models (VLMs) on medical image analysis.<n>MedVision spans 22 public datasets covering diverse anatomies and modalities, with 30.8 million image-annotation pairs.<n>We focus on three representative quantitative tasks: (1) detection of anatomical structures and abnormalities, (2) tumor/lesion (T/L) size estimation, and (3) angle/distance (A/D) measurement.<n>With supervised fine-tuning on MedVision, we significantly enhance their performance across detection, T/L estimation, and A/D measurement
• Score: 29.537915199171547
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., "Is this normal or abnormal?") or qualitative descriptive tasks. However, clinical decision-making often relies on quantitative assessments, such as measuring the size of a tumor or the angle of a joint, from which physicians draw their own diagnostic conclusions. This quantitative reasoning capability remains underexplored and poorly supported in existing VLMs. In this work, we introduce MedVision, a large-scale dataset and benchmark specifically designed to evaluate and improve VLMs on quantitative medical image analysis. MedVision spans 22 public datasets covering diverse anatomies and modalities, with 30.8 million image-annotation pairs. We focus on three representative quantitative tasks: (1) detection of anatomical structures and abnormalities, (2) tumor/lesion (T/L) size estimation, and (3) angle/distance (A/D) measurement. Our benchmarks show that current off-the-shelf VLMs perform poorly on these tasks. However, with supervised fine-tuning on MedVision, we significantly enhance their performance across detection, T/L estimation, and A/D measurement, demonstrating reduced error rates and improved precision. This work provides a foundation for developing VLMs with robust quantitative reasoning capabilities in medical imaging. Code and data are available at https://medvision-vlm.github.io.

Related papers

• ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding [67.57715989349471]
  Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge.<n>We introduce ClinFusion, a vision-centric MLLM designed for holistic medical understanding.<n>We show that ClinFusion sets a new state-of-the-art across a comprehensive suite of 2D and 3D multimodal medical benchmarks.
  arXiv  Detail & Related papers  (2026-07-27T17:59:49Z)
• MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models [83.50100003741628]
  Vision and language models (VLMs) hold immense promise to transform biomedical imaging.<n>We introduce the Massive Multimodal Biomedical Understanding (MMBU) benchmark.<n>It is the largest vision and language benchmark to date, covering 35 submodalities with rich structured metadata.
  arXiv  Detail & Related papers  (2026-06-04T20:24:47Z)
• DeepTumorVQA: A Hierarchical 3D CT Benchmark for Stage-Wise Evaluation of Medical VLMs and Tool-Augmented Agents [15.131996289027926]
  We propose a hierarchical benchmark that decomposes 3D CT reasoning into four stages: recognition, measurement, visual reasoning, and medical reasoning.<n>The benchmark contains 476K questions across 42 clinical subtypes on 9,262 3D CT volumes.
  arXiv  Detail & Related papers  (2026-05-10T17:57:57Z)
• TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models [54.48710348910535]
  Existing medical reasoning benchmarks primarily focus on analyzing a patient's condition based on an image from a single visit.<n>We introduce TemMed-Bench, the first benchmark designed for analyzing changes in patients' conditions between different clinical visits.
  arXiv  Detail & Related papers  (2025-09-29T17:51:26Z)
• Data-Efficient Fine-Tuning of Vision-Language Models for Diagnosis of Alzheimer's Disease [3.46857682956989]
  Medical vision-language models (Med-VLMs) have shown impressive results in tasks such as report generation and visual question answering.<n>Most existing models are typically trained from scratch or fine-tuned on large-scale 2D image-text pairs.<n>We propose a data-efficient fine-tuning pipeline to adapt 3D CT-based Med-VLMs for 3D MRI.
  arXiv  Detail & Related papers  (2025-09-09T11:36:21Z)
• Does DINOv3 Set a New Medical Vision Standard? [67.33543059306938]
  This report investigates whether DINOv3 can serve as a powerful unified encoder for medical vision tasks without domain-specific pre-training.<n>We benchmark DINOv3 across common medical vision tasks, including 2D/3D classification and segmentation.<n>Remarkably, it can even outperform medical-specific foundation models like BiomedCLIP and CT-Net on several tasks.
  arXiv  Detail & Related papers  (2025-09-08T09:28:57Z)
• How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study [16.84832179579428]
  Vision-Language Models (VLMs) trained on web-scale corpora excel at natural image tasks and are increasingly repurposed for healthcare.<n>We present a comprehensive evaluation of open-source general-purpose and medically specialised VLMs, across eight benchmarks.<n>First, large general-purpose models already match or surpass medical-specific counterparts on several benchmarks, demonstrating strong zero-shot transfer from natural to medical images.<n>Second, reasoning performance is consistently lower than understanding, highlighting a critical barrier to safe decision support.
  arXiv  Detail & Related papers  (2025-07-15T11:12:39Z)
• Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering [8.185551155349241]
  Vision-Language Models (VLMs) have shown promise in various 2D visual tasks, yet their readiness for 3D clinical diagnosis remains unclear.<n>We present DeepTumorVQA, a diagnostic visual question answering benchmark targeting abdominal tumors in CT scans.<n>It comprises 9,262 CT volumes (3.7M slices) from 17 public datasets, with 395K expert-level questions spanning four categories: Recognition, Measurement, Visual Reasoning, and Medical Reasoning.
  arXiv  Detail & Related papers  (2025-05-25T00:50:15Z)
• Point, Detect, Count: Multi-Task Medical Image Understanding with Instruction-Tuned Vision-Language Models [3.3091869879941687]
  We investigate fine-tuning Vision-Language Models (VLMs) for multi-task medical image understanding.<n>We reformulate each task into instruction-based prompts suitable for vision-language reasoning.<n>Results show that multi-task training improves robustness and accuracy.
  arXiv  Detail & Related papers  (2025-05-22T13:18:44Z)
• EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model [51.66031028717933]
  Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare.<n>Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data; (ii) Benchmark; and (iii) Model.<n>We propose the Eyecare Kit, which tackles the aforementioned three key challenges with the tailored dataset, benchmark and model.
  arXiv  Detail & Related papers  (2025-04-18T12:09:15Z)
• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM [48.16696073640864]
  We introduce OmniMedVQA, a novel comprehensive medical Visual Question Answering (VQA) benchmark. All images in this benchmark are sourced from authentic medical scenarios. We have found that existing LVLMs struggle to address these medical VQA problems effectively.
  arXiv  Detail & Related papers  (2024-02-14T13:51:56Z)
• A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis [87.25494411021066]
  GPT-4V's multimodal capability for medical image analysis is evaluated. It is found that GPT-4V excels in understanding medical images and generates high-quality radiology reports. It is found that its performance for medical visual grounding needs to be substantially improved.
  arXiv  Detail & Related papers  (2023-10-31T11:39:09Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)

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.