MedFILIP: Medical Fine-grained Language-Image Pre-training

• URL: http://arxiv.org/abs/2501.10775v1
• Date: Sat, 18 Jan 2025 14:08:33 GMT
• Title: MedFILIP: Medical Fine-grained Language-Image Pre-training
• Authors: Xinjie Liang, Xiangyu Li, Fanding Li, Jie Jiang, Qing Dong, Wei Wang, Kuanquan Wang, Suyu Dong, Gongning Luo, Shuo Li,
• Abstract summary: Existing methods struggle to accurately characterize associations between images and diseases. MedFILIP introduces medical image-specific knowledge through contrastive learning. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance.
• Score: 11.894318326422054
• License:
• Abstract: Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we propose MedFILIP, a fine-grained VLP model, introduces medical image-specific knowledge through contrastive learning, specifically: 1) An information extractor based on a large language model is proposed to decouple comprehensive disease details from reports, which excels in extracting disease deals through flexible prompt engineering, thereby effectively reducing text complexity while retaining rich information at a tiny cost. 2) A knowledge injector is proposed to construct relationships between categories and visual attributes, which help the model to make judgments based on image features, and fosters knowledge extrapolation to unfamiliar disease categories. 3) A semantic similarity matrix based on fine-grained annotations is proposed, providing smoother, information-richer labels, thus allowing fine-grained image-text alignment. 4) We validate MedFILIP on numerous datasets, e.g., RSNA-Pneumonia, NIH ChestX-ray14, VinBigData, and COVID-19. For single-label, multi-label, and fine-grained classification, our model achieves state-of-the-art performance, the classification accuracy has increased by a maximum of 6.69\%. The code is available in https://github.com/PerceptionComputingLab/MedFILIP.

Related papers

• LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model [55.80651780294357]
  State-of-the-art medical multi-modal large language models (med-MLLM) leverage instruction-following data in pre-training. LoGra-Med is a new multi-graph alignment algorithm that enforces triplet correlations across image modalities, conversation-based descriptions, and extended captions. Our results show LoGra-Med matches LLAVA-Med performance on 600K image-text pairs for Medical VQA and significantly outperforms it when trained on 10% of the data.
  arXiv  Detail & Related papers  (2024-10-03T15:52:03Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning [20.33625985769796]
  Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. We propose a Medical Language-Image Pre-training framework, which exploits the limited image-text medical data more efficiently. Our evaluation results show that MLIP outperforms previous work in zero/few-shot classification and few-shot segmentation tasks by a large margin.
  arXiv  Detail & Related papers  (2024-01-03T07:54:13Z)
• CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training [6.292642131180376]
  In this paper, we tackle the lack of image-text data in chest X-ray by expanding image-label pair as image-text pair via general prompt. We also design two contrastive losses, named ICL and TCL, for learning study-level characteristics of medical images and reports. Our model outperforms the state-of-the-art models trained under the same conditions.
  arXiv  Detail & Related papers  (2023-10-20T05:44:55Z)
• A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis [15.13309228766603]
  We propose a novel CLIP-based zero-shot medical image classification framework supplemented with ChatGPT for explainable diagnosis. The key idea is to query large language models (LLMs) with category names to automatically generate additional cues and knowledge. Extensive results on one private dataset and four public datasets along with detailed analysis demonstrate the effectiveness and explainability of our training-free zero-shot diagnosis pipeline.
  arXiv  Detail & Related papers  (2023-07-05T01:45:19Z)
• 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)
• KiUT: Knowledge-injected U-Transformer for Radiology Report Generation [10.139767157037829]
  Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively distill the information.
  arXiv  Detail & Related papers  (2023-06-20T07:27:28Z)
• 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)
• Positional Contrastive Learning for Volumetric Medical Image Segmentation [13.086140606803408]
  We propose a novel positional contrastive learning framework to generate contrastive data pairs. The proposed PCL method can substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting.
  arXiv  Detail & Related papers  (2021-06-16T22:15:28Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.