Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets

Crosby, Jennie; Rhines, Thomas; Li, Feng; MacMahon, Heber; Giger, Maryellen L.

doi:10.1117/12.2549709

Cited by 4 publications

(4 citation statements)

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“…Gooßen 101 compared classification and localisation performance of CNNs, multiple‐instance learning, and fully convolutional networks, with AUROCs of 0.96, 0.93 and 0.92, respectively. Crosby 102 increased spatial resolution by reducing the FOV to the lung apices and also using 256x256‐pixel patches for training. Using a pretrained CNN (VGG‐19), the AUROC for the apex‐based network was 0.80 and for the patch‐based network 0.73 in detecting a pneumothorax.…”

Section: Automatic Disease Detection On Cxr Imagesmentioning

confidence: 99%

Deep learning applied to automatic disease detection using chest X‐rays

Moses

2021

J Med Imag Rad Onc

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Deep learning (DL) has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs. This is important given the widespread use of CXRs across the world in diagnosing significant pathologies, and the lack of trained radiologists to report them. This review article introduces the basic concepts of DL as applied to CXR image analysis including basic deep neural network (DNN) structure, the use of transfer learning and the application of data augmentation. It then reviews the current literature on how DNN models have been applied to the detection of common CXR abnormalities (e.g. lung nodules, pneumonia, tuberculosis and pneumothorax) over the last few years. This includes DL approaches employed for the classification of multiple different diseases (multi‐class classification). Performance of different techniques and models and their comparison with human observers are presented. Some of the challenges facing DNN models, including their future implementation and relationships to radiologists, are also discussed.

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Section: Automatic Disease Detection On Cxr Imagesmentioning

confidence: 99%

Deep learning applied to automatic disease detection using chest X‐rays

Moses

2021

J Med Imag Rad Onc

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“…MIL breaks the input image into smaller parts (instances), makes individual predictions relating to those instances and combines this information to make a prediction for the whole image. Some studies that make use of MIL are (Crosby et al, 2020c;Schwab et al, 2020). Other topics within the literature include model uncertainty (Ul Abideen et al, 2020;Ghesu et al, 2019), quality of the CXR (Moradi et al, 2020;McManigle et al, 2020;Moradi et al, 2019a;Takaki et al, 2020;McManigle et al, 2020) and defence against adversarial attack (Li and Zhu, 2020;Anand et al, 2020;Xue et al, 2019).…”

Section: Image-level Predictionmentioning

confidence: 99%

Deep Learning for Chest X-ray Analysis: A Survey

Sogancioglu,

Çallı,

van Ginneken

et al. 2021

Preprint

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Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.

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“…Jennie et al 33 believed that the normal sizes of CXR images used in deep models are not sufficient to classify pneumothorax. They separated the two top thirds of each lung as apex images and extracted patches from them.…”

Section: Introductionmentioning

confidence: 99%

“…Jennie et al 33 . believed that the normal sizes of CXR images used in deep models are not sufficient to classify pneumothorax.…”

Section: Introductionmentioning

confidence: 99%

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

Yahyatabar,

Jouvet,

Cheriet

2023

J. Med. Imag.

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Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable.Approach: To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants. Results:The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model's predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images. Conclusions:This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.

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Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets

Cited by 4 publications

References 0 publications

Deep learning applied to automatic disease detection using chest X‐rays

Deep learning applied to automatic disease detection using chest X‐rays

Deep Learning for Chest X-ray Analysis: A Survey

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays

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