Characteristics of publicly available skin cancer image datasets: a systematic review

Wen, David; Khan, Saad; Ji‐Xu, Antonio; Ibrahim, Hassan; Smith, Luke A; Caballero, J. A.; Zepeda, Luis; Perez, Carlos de Blas; Denniston, Alastair K.O.; Liu, Xiaoxuan; Matin, Rubeta

doi:10.1016/s2589-7500(21)00252-1

Cited by 118 publications

(78 citation statements)

References 70 publications

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“…Additional curation together with additional interand intra-rater reliability measures may help to further enhance the datasets. However, dataset curation is a difficult and time-consuming task, and presents additional challenges in the form of label noise and artefacts which could affect the true accuracy of models trained on our data [34,35].…”

Section: Analysis On the Top-3 Resultsmentioning

confidence: 99%

Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

Cassidy,

Kendrick,

Reeves

et al. 2021

Preprint

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Diabetic foot ulcer classification systems use the presence of wound infection (bacteria present within the wound) and ischaemia (restricted blood supply) as vital clinical indicators for treatment and prediction of wound healing. Studies investigating the use of automated computerised methods of classifying infection and ischaemia within diabetic foot wounds are limited due to a paucity of publicly available datasets and severe data imbalance in those few that exist. The Diabetic Foot Ulcer Challenge 2021 provided participants with a more substantial dataset comprising a total of 15,683 diabetic foot ulcer patches, with 5,955 used for training, 5,734 used for testing and an additional 3,994 unlabelled patches to promote the development of semi-supervised and weakly-supervised deep learning techniques. This paper provides an evaluation of the methods used in the Diabetic Foot Ulcer Challenge 2021, and summarises the results obtained from each network. The best performing network was an ensemble of the results of the top 3 models, with a macro-average F1-score of 0.6307.

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Section: Analysis On the Top-3 Resultsmentioning

confidence: 99%

Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

Cassidy,

Kendrick,

Reeves

et al. 2021

Preprint

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“…Wilson et al investigated pedestrian detection with the BDD dataset and found poorer pedestrian prediction for darker skin tones that is not explained by confounding variables like time of day or occlusion [31]. Wen et al performed a systematic review of publicly available skin image datasets and found a substantial underrepresentation of darker skin types [30]. We believe that our approach could be used to recognize if images of people from underrepresented subpopulations map outside of the tested embedding region before harmful failures occur.…”

Section: Societal Impactmentioning

confidence: 85%

Mapping DNN Embedding Manifolds for Network Generalization Prediction

O'Brien¹,

Bukowski²,

Unberath³

et al. 2022

Preprint

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Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e.g., perception for self-driving vehicles or medical image analysis. Recently, the task of Network Generalization Prediction (NGP) has been proposed to predict how a DNN will generalize in a new operating domain. Previous NGP approaches have relied on labeled metadata and known distributions for the new operating domains. In this study, we propose the first NGP approach that predicts DNN performance based solely on how unlabeled images from an external operating domain map in the DNN embedding space. We demonstrate this technique for pedestrian, melanoma, and animal classification tasks and show state of the art NGP in 13 of 15 NGP tasks without requiring domain knowledge. Additionally, we show that our NGP embedding maps can be used to identify misclassified images when the DNN performance is poor.

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“…Edinburgh skin lesions dataset includes 1.3 k images of 10 lesions [ 77 ]. Wen et al [ 78 ] represented skin lesions related to 21 datasets (containing 1000 k images) and atlases that also included age, sex, region, ethnicity, and other factors.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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Characteristics of publicly available skin cancer image datasets: a systematic review

Cited by 118 publications

References 70 publications

Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary

Mapping DNN Embedding Manifolds for Network Generalization Prediction

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

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