Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

2021 ◽  
pp. 981-990
Author(s):  
Mostafa El Habib Daho ◽  
Amin Khouani ◽  
Mohammed El Amine Lazouni ◽  
Sidi Ahmed Mahmoudi
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Learning Model

scholarly journals Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia patients: A Deep Learning Perspective

2020 ◽  
Author(s):  
Myeongkyun Kang ◽  
Philip Chikontwe ◽  
Miguel Luna ◽  
Kyung Soo Hong ◽  
Jong Geol Jang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bacterial Pneumonia ◽  
Clinical Information ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Severity Classification ◽  
Ct Analysis ◽  
Deep Learning Model

ABSTRACTAs the number of COVID-19 patients has increased worldwide, many efforts have been made to find common patterns in CT images of COVID-19 patients and to confirm the relevance of these patterns against other clinical information. The aim of this paper is to propose a new method that allowed us to find patterns which observed on CTs of patients, and further we use these patterns for disease and severity diagnosis. For the experiment, we performed a retrospective cohort study of 170 confirmed patients with COVID-19 and bacterial pneumonia acquired at Yeungnam University hospital in Daegu, Korea. We extracted lesions inside the lungs from the CT images and classified whether these lesions were from COVID-19 patients or bacterial pneumonia patients by applying a deep learning model. From our experiments, we found 20 patterns that have a major effect on the classification performance of the deep learning model. Crazy-paving was extracted as a major pattern of bacterial pneumonia, while Ground-glass opacities (GGOs) in the peripheral lungs as that of COVID-19. Diffuse GGOs in the central and peripheral lungs was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia with 95% reported for severity classification. Chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. Moreover, the constructed patient level histogram with/without radiomics features showed feasibility and improved accuracy for both disease and severity classification with key clinical implications.

scholarly journals Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

2020 ◽  
Author(s):  
A. Amyar ◽  
R. Modzelewski ◽  
S. Ruan
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
The Novel ◽  
Imaging Analysis ◽  
Proposed Model ◽  
Fast Spreading ◽  
Novel Coronavirus ◽  
Deep Learning Model

ABSTRACTThe fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

Hybrid deep-learning model for volume segmentation of lung nodules in CT images

2020 ◽  
Author(s):  
Yifan Wang ◽  
Chuan Zhou ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Jun Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Lung Nodules ◽  
Volume Segmentation ◽  
Deep Learning Model

scholarly journals Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images

2020 ◽  
Vol 8 (6) ◽  
pp. 287-287
Author(s):  
Lei Yang ◽  
Wenjia Cai ◽  
Xiaoyu Yang ◽  
Haoshuai Zhu ◽  
Zhenguo Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Stage I ◽  
Preoperative Ct ◽  
Deep Learning Model

Deep Learning Model for Automated Kidney Stone Detection using Coronal CT Images

2021 ◽  
pp. 104569
Author(s):  
Kadir Yildirim ◽  
Pinar Gundogan Bozdag ◽  
Muhammed Talo ◽  
Ozal Yildirim ◽  
Murat Karabatak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Kidney Stone ◽  
Learning Model ◽  
Ct Images ◽  
Deep Learning Model ◽  
Stone Detection

scholarly journals Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Daryl L. X. Fung ◽  
Qian Liu ◽  
Judah Zammit ◽  
Carson Kai-Sang Leung ◽  
Pingzhao Hu
Keyword(s):  
Deep Learning ◽  
Causal Relationship ◽  
Learning Model ◽  
Ct Images ◽  
Ct Image ◽  
Underlying Diseases ◽  
Relationship Of ◽  
The Relationship ◽  
Deep Learning Model ◽  
Lung Ct

Abstract Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).

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