Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation

• URL: http://arxiv.org/abs/2603.04878v1
• Date: Thu, 05 Mar 2026 07:07:07 GMT
• Title: Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation
• Authors: Hong Liu, Dong Wei, Qiong Peng, Yawen Huang, Xian Wu, Yefeng Zheng, Liansheng Wang,
• Abstract summary: This work introduces a novel two-stage (structure- and report-learning) framework tailored for Computed Tomography Report Generation (CTRG)<n>In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss.<n>In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption.
• Score: 51.509572354327986
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computed Tomography Report Generation (CTRG) aims to automate the clinical radiology reporting process, thereby reducing the workload of report writing and facilitating patient care. While deep learning approaches have achieved remarkable advances in X-ray report generation, their effectiveness may be limited in CTRG due to larger data volumes of CT images and more intricate details required to describe them. This work introduces a novel two-stage (structure- and report-learning) framework tailored for CTRG featuring effective structure-wise image-text contrasting. In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss. In addition, text-text similarity-based soft pseudo targets are proposed to mitigate the impact of false negatives, i.e., semantically identical image structures and texts from non-paired images and reports. Thus, the model learns structure-level semantic correspondences between CT images and reports. Further, a dynamic, diversity-enhanced negative queue is proposed to guide the network in learning to discriminate various abnormalities. In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption. Also, a text decoder is added and trained for report generation.Our extensive experiments on two public datasets demonstrate that our framework establishes new state-of-the-art performance for CTRG in clinical efficiency, and its components are effective.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation [61.350584471060756]
  Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images.<n>We propose Self-Supervised Anatomical Consistency Learning (SS-ACL) to align generated reports with corresponding anatomical regions.<n>SS-ACL constructs a hierarchical anatomical graph inspired by the invariant top-down inclusion structure of human anatomy.
  arXiv  Detail & Related papers  (2025-09-30T08:59:06Z)
• Anatomical Attention Alignment representation for Radiology Report Generation [10.887845017050477]
  Anatomical Attention Alignment Network (A3Net) is a framework that enhance visual-textual understanding by constructing hyper-visual representations.<n>Our approach integrates a knowledge dictionary of anatomical structures with patch-level visual features, enabling the model to effectively associate image regions with their corresponding anatomical entities.<n> Experimental results on IU X-Ray and MIMIC-CXR datasets demonstrate that A3Net significantly improves both visual perception and text generation quality.
  arXiv  Detail & Related papers  (2025-05-12T15:54:50Z)
• RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining [64.66825253356869]
  We propose a novel methodology that leverages dense radiology reports to define image-wise similarity ordering at multiple granularities.<n>We construct two comprehensive medical imaging retrieval datasets: MIMIC-IR for Chest X-rays and CTRATE-IR for CT scans.<n>We develop two retrieval systems, RadIR-CXR and model-ChestCT, which demonstrate superior performance in traditional image-image and image-report retrieval tasks.
  arXiv  Detail & Related papers  (2025-03-06T17:43:03Z)
• Fake It Till You Make It: Using Synthetic Data and Domain Knowledge for Improved Text-Based Learning for LGE Detection [11.532639713283226]
  We use strategies rooted in domain knowledge to train a model for LGE detection using text from clinical reports.<n>We standardize the orientation of the images in an anatomy-informed way to enable better alignment of spatial and text features.<n> ablation studies are carried out to elucidate the contributions of each design component to the overall performance of the model.
  arXiv  Detail & Related papers  (2025-02-18T15:30:48Z)
• A Multimodal Approach Combining Structural and Cross-domain Textual Guidance for Weakly Supervised OCT Segmentation [12.948027961485536]
  We propose a novel Weakly Supervised Semantic (WSSS) approach that integrates structural guidance with text-driven strategies to generate high-quality pseudo labels. Our method achieves state-of-the-art performance, highlighting its potential to improve diagnostic accuracy and efficiency in medical imaging.
  arXiv  Detail & Related papers  (2024-11-19T16:20:27Z)
• A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models [4.023338734079828]
  Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support.
  arXiv  Detail & Related papers  (2024-03-27T19:43:45Z)
• Anatomical Structure-Guided Medical Vision-Language Pre-training [21.68719061251635]
  We propose an Anatomical Structure-Guided (ASG) framework for learning medical visual representations. For anatomical region, we design an automatic anatomical region-sentence alignment paradigm in collaboration with radiologists. For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample.
  arXiv  Detail & Related papers  (2024-03-14T11:29:47Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays [37.15474283789249]
  We propose a method to predict clinical findings defined by sentences in structured reporting templates. The approach involves training a contrastive language-image model using chest X-rays and related free-text radiological reports. Results show that even with limited image-level annotations for training, the method can accomplish the structured reporting tasks of severity assessment of cardiomegaly and localizing pathologies in chest X-rays.
  arXiv  Detail & Related papers  (2022-03-29T16:31:39Z)

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.