Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis

• URL: http://arxiv.org/abs/2405.08932v1
• Date: Tue, 14 May 2024 19:53:20 GMT
• Title: Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis
• Authors: Alexandre Englebert, Anne-Sophie Collin, Olivier Cornu, Christophe De Vleeschouwer,
• Abstract summary: This paper proposes leveraging vision-language pretraining on bone X-rays paired with French reports. It is the first study to integrate French reports to shape the embedding space devoted to bone X-Rays representations.
• Score: 53.809054774037214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes leveraging vision-language pretraining on bone X-rays paired with French reports to address downstream tasks of interest on bone radiography. A practical processing pipeline is introduced to anonymize and process French medical reports. Pretraining then consists in the self-supervised alignment of visual and textual embedding spaces derived from deep model encoders. The resulting image encoder is then used to handle various downstream tasks, including quantification of osteoarthritis, estimation of bone age on pediatric wrists, bone fracture and anomaly detection. Our approach demonstrates competitive performance on downstream tasks, compared to alternatives requiring a significantly larger amount of human expert annotations. Our work stands as the first study to integrate French reports to shape the embedding space devoted to bone X-Rays representations, capitalizing on the large quantity of paired images and reports data available in an hospital. By relying on generic vision-laguage deep models in a language-specific scenario, it contributes to the deployement of vision models for wider healthcare applications.

Related papers

• CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting [0.0]
  We evaluate the publicly available, state of the art, foundational vision-language models for chest X-ray interpretation. We find that vision-language models often hallucinate with confident language, which slows down clinical interpretation. We develop an agent-based vision-language approach for report generation using CheXagent's linear probes and BioViL-T's phrase grounding tools.
  arXiv  Detail & Related papers  (2024-07-11T18:39:19Z)
• Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation [10.46031380503486]
  We introduce a novel method, textbfStructural textbfEntities extraction and patient indications textbfIncorporation (SEI) for chest X-ray report generation. We employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports. We propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications.
  arXiv  Detail & Related papers  (2024-05-23T01:29:47Z)
• 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)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• 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)
• Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation [19.69560434388278]
  The goal of medical report generation is to accurately capture and describe the image findings. Previous works pretrain their visual encoding neural networks with large datasets in different domains. We propose a framework that uses a contrastive learning approach to pretrain the visual encoder and requires no additional meta information.
  arXiv  Detail & Related papers  (2022-09-04T12:07:19Z)
• Variational Topic Inference for Chest X-Ray Report Generation [102.04931207504173]
  Report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successfully caption natural images. We propose variational topic inference for automatic report generation.
  arXiv  Detail & Related papers  (2021-07-15T13:34:38Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)

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.