Pneumonia Detection in Chest Radiographs
In this work, we describe our approach to pneumonia classification and localization in chest radiographs. This method uses only \emph{open-source} deep learning object detection and is based on CoupleNet, a fully convolutional network which incorporates global and local features for object detection. Our approach achieves robustness through critical modifications of the training process and a novel ensembling algorithm which merges bounding boxes from several models. We tested our detection algorithm tested on a dataset of 3000 chest radiographs as part of the 2018 RSNA Pneumonia Challenge; our solution was recognized as a winning entry in a contest which attracted more than 1400 participants worldwide.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationObjectobject-detectionObject DetectionPneumonia DetectionSimilar Papers 제목 키워드 기반
An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection
Pediatric pneumonia remains a significant global threat, posing a larger mortality risk than any other communicable disease. According to UNICEF, it is a leading cause of mortality in children under five and requires pro…
Data AugmentationPneumonia DetectionPOLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)
The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of t…
COVID-19 DiagnosisA Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs
While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a variational quantum circuit could be integr…
COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and se…
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure
Objective: When patients develop acute respiratory failure, accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be…
BIG-bench Machine LearningDecision MakingDiagnosticMedical Diagnosis+1