Deep Regression by Feature Regularization for COVID-19 Severity Prediction

Tricarico, Davide; Chaudhry, Hafiza Ayesha Hoor; Fiandrotti, Attilio; Grangetto, Marco

doi:10.1007/978-3-031-13324-4_42

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The EIDOSlab_Unito team from the University of Turin, Italy [ 39 ] proposed a contrastive learning-based approach for the CIPE. Their approach consists of four main steps: (i) the input slices were preprocessed using image resizing and pixel intensity scaling to reduce the brightness and pixel intensity contrast between the training and test splits; (ii) DenseNet-121 [ 24 ] architecture was used as a feature projector to the 1024-dimensional target space; (iii) a distance loss function was proposed to correlate the distances between the samples in the deep feature space and their GT values in the target space (i.e., the COVID-19 level of infection); and (iv) the COVID-19 percentage estimation of a new image, the query, is computed by averaging the values from the nearest neighbours by means of Euclidean distance in the projection feature space from images in the training set (the reference set).…”

Section: Participating Teamsmentioning

confidence: 99%

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

Bougourzi,

Distante,

Dornaika

et al. 2024

Sensors

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COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

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Section: Participating Teamsmentioning

confidence: 99%

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

Bougourzi,

Distante,

Dornaika

et al. 2024

Sensors

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“… Percentage estimation from CT images The augmentation techniques helped achieving the desired prediction performance. Tricarico, Chaudhry, Fiandrotti, and Grangetto (2022) Proposed feature regularization based deep regression approach for the severity prediction task. Percentage estimation from CT scan.…”

Section: Related Workmentioning

confidence: 99%

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Chauhan

Bedi²

2023

Expert Systems with Applications

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Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Ajagbe

Adigun

2023

Multimed Tools Appl

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Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.

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Deep Regression by Feature Regularization for COVID-19 Severity Prediction

Cited by 3 publications

References 19 publications

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

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