Attention gated networks: Learning to leverage salient regions in medical images.

Background
Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.

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Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

This paper considers the task of thorax disease classification on chest X-ray images Existing methods generally use the global image as input for network learning Such a strategy is limited in two aspects 1) A thorax disease usually happens in (small) localized areas which are disease specific Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN) AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch Specifically, we first learn a global CNN branch using global images Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image The local region is used for training a local CNN branch Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch The Comprehensive experiment is conducted on the ChestX-ray14 dataset We first report a strong global baseline producing an average AUC of 0841 with ResNet-50 as backbone After combining the local cues with the global information, AG-CNN improves the average AUC to 0868 While DenseNet-121 is used, the average AUC achieves 0871, which is a new state of the art in the community

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification.

Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.

Robert Bakewell

Papers

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks