A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images

Voulodimos, Athanasios; Protopapadakis, Eftychios; Katsamenis, Iason; Doulamis, Anastasios; Doulamis, Nikolaos

doi:10.3390/s21062215

Cited by 68 publications

(35 citation statements)

References 59 publications

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“…There are a few recent studies that used explainable AI in chest CT segmentation and classification of COVID-19 patients, such as these based on class activation map [52], few-shot learning [53], and the shapely addictive explanations framework [29]. Another potential future extension of this work is to use explainable AI frameworks to explain the model's logics and decision-making processes, thereby unlocking the black box of deep learning and helping the end users to understand the models better.…”

Section: Discussionmentioning

confidence: 99%

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

et al. 2021

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Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

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Section: Discussionmentioning

confidence: 99%

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

et al. 2021

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“…The state-of-the-Art methods using CT scans can be classified into two main tasks: COVID-19 recognition [5,6,[14][15][16] and COVID-19 segmentation [7,8,17,18]. In [19], Zheng, C. et al proposed the DeCoVNet approach, which is based on 3D deep convolutional neural Network to Detect COVID-19 (DeCoVNet) from CT volumes.…”

Section: Related Workmentioning

confidence: 99%

“…To deal with the limitation of the training data for segmenting COVID-19 infection, Athanasios. V. et al introduced the few-shot learning (FSL) concept of network model training using a very small number of samples [18]. They explored the efficiency of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the UNet.…”

Section: Related Workmentioning

confidence: 99%

Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans

et al. 2021

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COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state.

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“…In [40], the effectiveness of few-shot learning in U-Net architectures was investigated, which allows for dynamic fine-tuning of the network weights when few new samples are introduced into the U-Net. The results of the experiments show that the accuracy of segmenting COVID-19-infected lung areas has improved.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Khan

Alhaisoni

Tariq

et al. 2021

Sensors

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In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

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A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images

Cited by 68 publications

References 59 publications

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

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