Comprehensive language-image pre-training for 3D medical image understanding

• URL: http://arxiv.org/abs/2510.15042v1
• Date: Thu, 16 Oct 2025 18:01:31 GMT
• Title: Comprehensive language-image pre-training for 3D medical image understanding
• Authors: Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao, Sam Bond-Taylor, Harshita Sharma, Maximilian Ilse, Cynthia Lo, Olesya Melnichenko, Noel C. F. Codella, Maria Teodora Wetscherek, Klaus H. Maier-Hein, Panagiotis Korfiatis, Valentina Salvatelli, Javier Alvarez-Valle, Fernando Pérez-García,
• Abstract summary: Vision-language pre-training, i.e., aligning images with paired text, is a powerful paradigm to create encoders.<n>We develop the Comprehensive Language-image Pre-training (COLIPRI) encoder family.<n>Our COLIPRI encoders achieve state-of-the-art performance in report generation, classification probing, and zero-shot classification.
• Score: 40.12276593119101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language pre-training, i.e., aligning images with paired text, is a powerful paradigm to create encoders that can be directly used for tasks such as classification and retrieval, and for downstream tasks such as segmentation and report generation. In the 3D medical image domain, these capabilities allow vision-language encoders (VLEs) to support radiologists by retrieving patients with similar abnormalities or predicting likelihoods of abnormality. While the methodology holds promise, data availability limits the capabilities of current 3D VLEs. In this paper, we alleviate the lack of data by injecting additional inductive biases: introducing a report generation objective and pairing vision-language pre-training with vision-only pre-training. This allows us to leverage both image-only and paired image-text 3D datasets, increasing the total amount of data to which our model is exposed. Through these additional inductive biases, paired with best practices of the 3D medical imaging domain, we develop the Comprehensive Language-image Pre-training (COLIPRI) encoder family. Our COLIPRI encoders achieve state-of-the-art performance in report generation, classification probing, and zero-shot classification, and remain competitive for semantic segmentation.

Related papers

• 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)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework.<n>Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• Surgical Text-to-Image Generation [1.958913666074613]
  We adapt text-to-image generative models for the surgical domain using the CholecT50 dataset.<n>We develop Surgical Imagen to generate photorealistic and activity-aligned surgical images from triplet-based textual prompts.
  arXiv  Detail & Related papers  (2024-07-12T12:49:11Z)
• CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios [53.94122089629544]
  We introduce CT-GLIP (Grounded Language-Image Pretraining with CT scans), a novel method that constructs organ-level image-text pairs to enhance multimodal contrastive learning. Our method, trained on a multimodal CT dataset comprising 44,011 organ-level vision-text pairs from 17,702 patients across 104 organs, demonstrates it can identify organs and abnormalities in a zero-shot manner using natural languages.
  arXiv  Detail & Related papers  (2024-04-23T17:59:01Z)
• Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training [15.790435273150083]
  We introduce the backbone-agnostic Adaptor framework, which preserves medical knowledge in pre-trained image and text encoders by keeping them frozen. Our framework delivers competitive performance while cutting trainable parameters by over 90% compared to current pre-training approaches.
  arXiv  Detail & Related papers  (2024-01-02T12:14:41Z)
• Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images [9.86468773903613]
  Medical Vision-Language Pre-training learns representations jointly from medical images and paired radiology reports. We replace real medical images with their synthetic equivalents, generated from authentic medical reports. Our empirical evaluation reveals that the performance achieved through synthetic data is on par with or even exceeds that obtained with real images.
  arXiv  Detail & Related papers  (2023-10-10T21:29:41Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing [53.89917396428747]
  Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. We explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
  arXiv  Detail & Related papers  (2023-01-11T16:35:33Z)
• Self-Paced Contrastive Learning for Semi-supervisedMedical Image Segmentation with Meta-labels [6.349708371894538]
  We propose to adapt contrastive learning to work with meta-label annotations. We use the meta-labels for pre-training the image encoder as well as to regularize a semi-supervised training. Results on three different medical image segmentation datasets show that our approach highly boosts the performance of a model trained on a few scans.
  arXiv  Detail & Related papers  (2021-07-29T04:30:46Z)

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.