Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images

Calderon-Ramirez, Saul; Shengxiang-Yang,; Moemeni, Armaghan; Elizondo, David; Colreavy‐Donnelly, Simon; Fernando, Chavarria-Estrada, Luis; Molina‐Cabello, Miguel A.

doi:10.48550/arxiv.2008.08496

Cited by 4 publications

(5 citation statements)

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“…• Prior probability shift: The density of the targets in S l is different to the real target densities in S u (increasing the possibility of sampling noise). Class imbalance in the labelled dataset S l is a special case of this setting, as discussed in [8]. • Covariate shift: The labelled dataset S l might sample a different density of the features when compared to the unlabelled dataset S u , causing a distribution mismatch between the two datasets.…”

Section: B Distribution Mismatch In Ssdlmentioning

confidence: 99%

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets

Ramírez

Oala

Torrentes-Barrena³

et al. 2023

IEEE Trans. Artif. Intell.

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Semi-supervised deep learning (SSDL) is a popular strategy to leverage unlabelled data for machine learning when labelled data is not readily available. In real-world scenarios, different unlabelled data sources are usually available, with varying degrees of distribution mismatch regarding the labelled datasets. It begs the question which unlabelled dataset to choose for good SSDL outcomes. Oftentimes, semantic heuristics are used to match unlabelled data with labelled data. However, a quantitative and systematic approach to this selection problem would be preferable. In this work, we first test the SSDL MixMatch algorithm under various distribution mismatch configurations to study the impact on SSDL accuracy. Then, we propose a quantitative unlabelled dataset selection heuristic based on dataset dissimilarity measures. These are designed to systematically assess how distribution mismatch between the labelled and unlabelled datasets affects MixMatch performance. We refer to our proposed method as deep dataset dissimilarity measures (DeDiMs), designed to compare labelled and unlabelled datasets. They use the feature space of a generic Wide-ResNet, can be applied prior to learning, are quick to evaluate and model agnostic. The strong correlation in our tests between MixMatch accuracy and the proposed DeDiMs suggests that this approach can be a good fit for quantitatively ranking different unlabelled datasets prior to SSDL training.Impact Statement-Semi-supervised deep learning is a technique for training a deep learning model when few labelled observations are available, leveraging unlabelled datasets. Different unlabelled data sources may be available, introducing the possibility for distribution mismatches between the labelled and unlabelled datasets. In this work we assess the impact of distribution mismatches on the outcomes of the semi-supervised MixMatch algorithm. We propose a set of simple feature-space density dataset distances, referred to as deep dataset dissimilarity measures (DeDiMs). In our extensive test-bed, the evaluated DeDiMs yield Manuscript

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Section: B Distribution Mismatch In Ssdlmentioning

confidence: 99%

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets

Ramírez

Oala

Torrentes-Barrena³

et al. 2023

IEEE Trans. Artif. Intell.

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“…Deep learning (DL) algorithms have been extensively applied for COVID-19 detection/segmentation of infected pneumonia regions from HRCTs and CXRs [12][13][14][15][16]. Shiri [12] built a residual network to develop a fast, consistent, robust and human error immune framework for lung and pneumonia lesion detection and quantification.…”

Section: Sars-cov-2 Disease (Covid-19mentioning

confidence: 99%

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

et al. 2022

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Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.

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“…The rationale of these semi-supervised learning models is to enrich the supervision signals by exploiting the knowledge learned on unlabeled data [41], or regularize the network by enforcing smooth and consistent classification boundaries [40]. Regarding COVID-19 research such as COVID-19 image classification and image segmentation, semi-supervised learning is employed to resolve the lacking of labeled data [42], [43], [44], [45], [46], [47]. However, for COVID-19 image classification, these studies [42], [43], [44] have not comprehensively examined the model performance on a large-scale of X-ray image dataset such as COVIDx [20] by comparing with the state-of-the-art, especially for the case of very few labeled data such as less than 10% labeled data.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding COVID-19 research such as COVID-19 image classification and image segmentation, semi-supervised learning is employed to resolve the lacking of labeled data [42], [43], [44], [45], [46], [47]. However, for COVID-19 image classification, these studies [42], [43], [44] have not comprehensively examined the model performance on a large-scale of X-ray image dataset such as COVIDx [20] by comparing with the state-of-the-art, especially for the case of very few labeled data such as less than 10% labeled data. This paper proposed a semi-supervised deep learning model for COVID-19 image classification and checked out the model performance systematically on the COVIDx [20] dataset.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Learning for COVID-19 Image Classification via ResNet

Nwosu¹,

Li²,

Qian³

et al. 2021

Preprint

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Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from Xray imaging datasets. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semisupervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images.

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Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images

Cited by 4 publications

References 0 publications

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets

Dataset Similarity to Assess Semisupervised Learning Under Distribution Mismatch Between the Labeled and Unlabeled Datasets

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Semi-supervised Learning for COVID-19 Image Classification via ResNet

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