Automated Cardiothoracic Ratio Calculation and Cardiomegaly Detection using Deep Learning Approach

Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly

We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs. We develop two separate models to demarcate the heart and chest regions in an X-ray image using bounding boxes and use their outputs to calculate the cardiothoracic ratio. We obtain a sensitivity of 0.96 at a specificity of 0.81 with a mean absolute error of 0.0209 on a held-out test dataset and a sensitivity of 0.84 at a specificity of 0.97 with a mean absolute error of 0.018 on an independent dataset from a different hospital. We also compa…

Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline

Chest x-rays are the most common radiology studies for diagnosing lung and heart disease. Hence, a system for automated pre-reporting of pathologic findings on chest x-rays would greatly enhance radiologists' productivity. To this end, we investigate a deep-learning framework with novel training schemes for classification of different thoracic pathology labels from chest x-rays. We use the currently largest publicly available annotated dataset ChestX-ray14 of 112,120 chest radiographs of 30,805 patients. Each image was annotated with either a 'NoFinding' class, or one or more of 14 thoracic pa…

基于多模型深度学习方法的胸部X光报告自动生成器

Automated Chest X-Ray Report Generator Using Multi-Model Deep Learning Approach

针对放射科医生胸部X光解读难度大的问题，提出一种基于多模型深度学习的报告自动生成系统，通过预处理图像、多二分类模型检测三类异常并拼接结果生成报告，可减轻医生负担、提升诊断准确率。

基于胸部X光片与密集卷积网络的多标签胸部疾病分类

Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs

本文提出基于DenseNet的多标签胸部疾病预测模型，可同时检测多种病理，并通过热力图可视化决策依据，在心脏肥大等疾病上取得较高AUC和准确率。

通过对比学习提升胸部X光自动诊断的公平性

Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning

针对放射学领域CXR诊断模型公平性提升方法不足的问题，提出基于监督对比学习的AI模型，通过正负样本生成公平嵌入再微调，在两个公开数据集上验证其可显著降低各子群体的诊断偏差。

基于改进的CheXNet框架的增强胸部疾病分类

Enhanced Chest Disease Classification Using an Improved CheXNet Framework with EfficientNetV2-M and Optimization-Driven Learning

本文提出基于改进CheXNet与EfficientNetV2-M的胸部疾病分类框架，通过优化训练方法提升准确率，达到96.45%的测试准确率，显著提升关键疾病检测性能。