MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Kollias, Dimitrios; Arsenos, Anastasios; Soukissian, Levon; Kollias, Stefanos

doi:10.48550/arxiv.2106.07524

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…They also validate the generalization ability of their method and use saliency map visualizations [61] for interpretability. Similarly, COVID-ViT [65] is another ViT-based model for classifying COVID from non-COVID images in the MIA-COVID19 challenge [66]. Their experiments on 3D CT lung images demonstrate the ViT-based approach's superiority over the DenseNet baseline [67] in terms of F1 score.…”

Section: Interpretable Vision Transformermentioning

confidence: 99%

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Lai

2024

BioMedInformatics

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Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.

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Section: Interpretable Vision Transformermentioning

confidence: 99%

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Lai

2024

BioMedInformatics

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“…We conducted the experiments to validate the effectiveness of our model on the COVID19-CT-Database (COV19-CT-DB) [20,21,17,19,18,16]. This dataset is provided to benchmark the models on the 2nd COV19D [17,18,16], which makes the COV19-CT-DB appealing to validate our methods because it was not built-up using RT-PCR results as ground truth labels. Further information on the dataset is available in [16].…”

Section: Datasets Descriptionmentioning

confidence: 99%

Representation Learning with Information Theory for COVID-19 Detection

Berenguer¹,

Mukherjee²,

Bossa³

et al. 2022

Preprint

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Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation learning. However, the inability of deep models to generalize to unseen data can quickly overfit intricate patterns. Thereby, we can conveniently implement strategies to aid deep models in discovering useful priors from data to learn their intrinsic properties. Our model, which we call a dual role network (DRN), uses a dependency maximization approach based on Least Squared Mutual Information (LSMI). LSMI leverages dependency measures to ensure representation invariance and local smoothness. While prior works have used information theory dependency measures like mutual information, these are known to be computationally expensive due to the density estimation step. In contrast, our proposed DRN with LSMI formulation does not require the density estimation step and can be used as an alternative to approximate mutual information. Experiments on the CT based COVID-19 Detection and COVID-19 Severity Detection benchmarks of the 2nd COV19D competition [16] demonstrate the effectiveness of our method compared to the baseline method of such competition.

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“…Early in the pandemic, attempts to use deep learning showed significant promise for accurate detection of COVID-19 [2,11,13]. To further this area of research, the 'AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D) was created as part of the International Conference on Computer Vision (ICCV) in 2021 [10]. This competition sought submissions to predict the presence of COVID-19 in a dataset of CT scan cross-sectional images.…”

Section: Introductionmentioning

confidence: 99%

Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets

Turnbull¹

2022

Preprint

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Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 0.9476 on the validation set for the task of detecting the presence of COVID19. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.7552. Both results improve on the baseline results of the 'AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D) in 2022.

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MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Cited by 4 publications

References 12 publications

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care

Representation Learning with Information Theory for COVID-19 Detection

Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets

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