Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment

• URL: http://arxiv.org/abs/2503.19860v1
• Date: Tue, 25 Mar 2025 17:26:17 GMT
• Title: Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment
• Authors: Junzhi Ning, Dominic Marshall, Yijian Gao, Xiaodan Xing Yang Nan, Yingying Fang, Sheng Zhang, Matthieu Komorowski, Guang Yang,
• Abstract summary: Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases.<n> lung opac- ities in CXRs frequently obscure anatomical structures, impeding clear identification of lung borders and complicating the localization of pathology.<n>This study proposes an unpaired CXR translation framework that converts CXRs with lung opacities into counterparts without lung opacities while preserving semantic features.
• Score: 7.3913562188704045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opac- ities in CXRs frequently obscure anatomical structures, impeding clear identification of lung borders and complicating the localization of pathology. This challenge significantly hampers segmentation accuracy and precise lesion identification, which are crucial for diagnosis. To tackle these issues, our study proposes an unpaired CXR translation framework that converts CXRs with lung opacities into counterparts without lung opacities while preserving semantic features. Central to our approach is the use of adaptive activation masks to selectively modify opacity regions in lung CXRs. Cross-domain alignment ensures translated CXRs without opacity issues align with feature maps and prediction labels from a pre-trained CXR lesion classifier, facilitating the interpretability of the translation process. We validate our method using RSNA, MIMIC-CXR-JPG and JSRT datasets, demonstrating superior translation quality through lower Frechet Inception Distance (FID) and Kernel Inception Distance (KID) scores compared to existing meth- ods (FID: 67.18 vs. 210.4, KID: 0.01604 vs. 0.225). Evaluation on RSNA opacity, MIMIC acute respiratory distress syndrome (ARDS) patient CXRs and JSRT CXRs show our method enhances segmentation accuracy of lung borders and improves lesion classification, further underscoring its potential in clinical settings (RSNA: mIoU: 76.58% vs. 62.58%, Sensitivity: 85.58% vs. 77.03%; MIMIC ARDS: mIoU: 86.20% vs. 72.07%, Sensitivity: 92.68% vs. 86.85%; JSRT: mIoU: 91.08% vs. 85.6%, Sensitivity: 97.62% vs. 95.04%). Our approach advances CXR imaging analysis, especially in investigating segmentation impacts through image translation techniques.

Related papers

• Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learning [19.948079693716075]
  Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis. We evaluated the methods on two datasets: the Medical Imaging and Data Resource Center (MIDRC) dataset with 77,887 CXR images and the NIH Chest X-ray dataset with 112,120 CXR images.
  arXiv  Detail & Related papers  (2024-01-25T20:03:57Z)
• ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging [41.202147558260336]
  We propose a framework that employs saliency maps to obtain soft spatial attention masks that modulate the image features at different scales. ACAT increases the baseline classification accuracy of lesions in brain CT scans from 71.39% to 72.55% and of COVID-19 related findings in lung CT scans from 67.71% to 70.84%.
  arXiv  Detail & Related papers  (2023-03-27T17:43:57Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors [49.005337470305584]
  During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. The variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. We propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases.
  arXiv  Detail & Related papers  (2022-08-22T13:57:04Z)
• A Deep Learning Based Workflow for Detection of Lung Nodules With Chest Radiograph [0.0]
  We built a segmentation model to identify lung areas from CXRs, and sliced them into 16 patches. These labeled patches were then used to train finetune a deep neural network(DNN) model, classifying the patches as positive or negative.
  arXiv  Detail & Related papers  (2021-12-19T16:19:46Z)
• A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images [0.0]
  COVID-19 was first identified in Wuhan and quickly spread worldwide. Most accessible method for COVID-19 identification is RT-PCR. Compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. DenseNet169 was employed to extract features from X-ray images.
  arXiv  Detail & Related papers  (2021-09-30T16:10:31Z)
• COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images [58.35627258364233]
  Use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow. We introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images. benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries.
  arXiv  Detail & Related papers  (2021-05-14T04:29:21Z)
• COVID-19 Infection Localization and Severity Grading from Chest X-ray Images [3.4546388019336143]
  Coronavirus disease 2019 (COVID-19) has been the main agenda of the whole world, since it came into sight in December 2019. We have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples. The proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.
  arXiv  Detail & Related papers  (2021-03-14T18:06:06Z)
• Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images [4.457871213347773]
  This paper explores the effect of various popular image enhancement techniques and states the effect of each of them on the detection performance. We have compiled the largest X-ray dataset called COVQU-20, consisting of 18,479 normal, non-COVID lung opacity and COVID-19 CXR images. The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96.29%, 96.28%, 96.29%, 96.28% and 96.27% respectively.
  arXiv  Detail & Related papers  (2020-11-25T20:58:27Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation [95.57532063232198]
  coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries. To control the infection, identifying and separating the infected people is the most crucial step. This paper develops a novel Joint Classification and (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis.
  arXiv  Detail & Related papers  (2020-04-15T12:30:40Z)
• Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images [54.919022945740515]
  The aim of this study is to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Several quantitative features, which have the potential to reflect the severity of COVID-19, were revealed.
  arXiv  Detail & Related papers  (2020-03-26T15:49:32Z)

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.