Large-scale Long-tailed Disease Diagnosis on Radiology Images

• URL: http://arxiv.org/abs/2312.16151v3
• Date: Sun, 16 Jun 2024 15:35:52 GMT
• Title: Large-scale Long-tailed Disease Diagnosis on Radiology Images
• Authors: Qiaoyu Zheng, Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Lisong Dai, Hengyu Guan, Yuehua Li, Ya Zhang, Yanfeng Wang, Weidi Xie,
• Abstract summary: RadDiag is a foundational model supporting 2D and 3D inputs across various modalities and anatomies. Our dataset, RP3D-DiagDS, contains 40,936 cases with 195,010 scans covering 5,568 disorders.
• Score: 51.453990034460304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing a generalist radiology diagnosis system can greatly enhance clinical diagnostics. In this paper, we introduce RadDiag, a foundational model supporting 2D and 3D inputs across various modalities and anatomies, using a transformer-based fusion module for comprehensive disease diagnosis. Due to patient privacy concerns and the lack of large-scale radiology diagnosis datasets, we utilize high-quality, clinician-reviewed radiological images available online with diagnosis labels. Our dataset, RP3D-DiagDS, contains 40,936 cases with 195,010 scans covering 5,568 disorders (930 unique ICD-10-CM codes). Experimentally, our RadDiag achieves 95.14% AUC on internal evaluation with the knowledge-enhancement strategy. Additionally, RadDiag can be zero-shot applied or fine-tuned to external diagnosis datasets sourced from various hospitals, demonstrating state-of-the-art results. In conclusion, we show that publicly shared medical data on the Internet is a tremendous and valuable resource that can potentially support building a generalist AI for healthcare.

Related papers

• Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses [29.70102468004044]
  We introduce a pipeline, TAILOR, that builds a knowledge-driven generative model to produce tailored synthetic data. The generative model, using 3,749 lesions as source data, can generate millions of breast-US images, especially for error-prone rare cases. In the prospective external evaluation, our diagnostic model outperforms the average performance of nine radiologists by 33.5% in specificity with the same sensitivity.
  arXiv  Detail & Related papers  (2024-07-23T16:49:01Z)
• A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation [12.617587827105496]
  This research aims to bridge the gap by providing publicly accessible datasets and reliable tools for medical diagnosis. We curated a diverse dataset of lung Computed Tomography (CT) images, comprising 330 annotated nodules (nodules are labeled as bounding boxes) from 95 distinct patients. These promising results demonstrate that the dataset has a feasible application and further facilitate intelligent auxiliary diagnosis.
  arXiv  Detail & Related papers  (2024-06-26T06:39:11Z)
• AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation [5.492165569390342]
  This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The proposed detection model is based on a two-step approach that, paired with state-of-the-art debiasing, provides reliable results.
  arXiv  Detail & Related papers  (2024-05-19T16:06:26Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data [66.9359934608229]
  This study aims to initiate the development of Radiology Foundation Model, termed as RadFM. To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans. We propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis.
  arXiv  Detail & Related papers  (2023-08-04T17:00:38Z)
• BMAD: Benchmarks for Medical Anomaly Detection [51.22159321912891]
  Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. We introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images.
  arXiv  Detail & Related papers  (2023-06-20T20:23:46Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning [3.0797300440355997]
  A method based on pre-trained deep neural networks is presented. It has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. A dataset containing 3163 images from 189 patients has been collected and labeled by physicians.
  arXiv  Detail & Related papers  (2021-04-24T13:12:20Z)
• Deep Learning in Detection and Diagnosis of Covid-19 using Radiology Modalities: A Systematic Review [0.0]
  Early detection and diagnosis of Covid-19 is one of the main challenges in the epidemic of Covid-19. Medical and computer researchers tended to use machine-learning models to analyze radiology images. Deep learning Based models have an extraordinary capacity to achieve an accurate and efficient system for the detection and diagnosis of Covid-19.
  arXiv  Detail & Related papers  (2020-12-21T18:54:01Z)

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.