21 Randomized assessment of strategies for self-detection in the general population: failure of ABCD, success of cognitive approach

Girardi, S; Gouvernet, Joanny; Richard, M.‐A.; Grob, Jean Jacques

doi:10.1097/00008390-200408000-00037

Cited by 3 publications

(2 citation statements)

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“…These features were then used to train an SVM for classification with feature selection [13]. However, the ABCDE criteria are not the best features to use for melanoma detection [14]. Moreover, these features are assimilated to low-level features in CAD systems, which can limit the overall accuracy of the CADs.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

Gouabou

Damoiseaux

Monnier

et al. 2021

Sensors

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The early detection of melanoma is the most efficient way to reduce its mortality rate. Dermatologists achieve this task with the help of dermoscopy, a non-invasive tool allowing the visualization of patterns of skin lesions. Computer-aided diagnosis (CAD) systems developed on dermoscopic images are needed to assist dermatologists. These systems rely mainly on multiclass classification approaches. However, the multiclass classification of skin lesions by an automated system remains a challenging task. Decomposing a multiclass problem into a binary problem can reduce the complexity of the initial problem and increase the overall performance. This paper proposes a CAD system to classify dermoscopic images into three diagnosis classes: melanoma, nevi, and seborrheic keratosis. We introduce a novel ensemble scheme of convolutional neural networks (CNNs), inspired by decomposition and ensemble methods, to improve the performance of the CAD system. Unlike conventional ensemble methods, we use a directed acyclic graph to aggregate binary CNNs for the melanoma detection task. On the ISIC 2018 public dataset, our method achieves the best balanced accuracy (76.6%) among multiclass CNNs, an ensemble of multiclass CNNs with classical aggregation methods, and other related works. Our results reveal that the directed acyclic graph is a meaningful approach to develop a reliable and robust automated diagnosis system for the multiclass classification of dermoscopic images.

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Section: Introductionmentioning

confidence: 99%

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

Gouabou

Damoiseaux

Monnier

et al. 2021

Sensors

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“…It has already been suggested that the melanoma detection process used by dermatologists is not based on an analytical process, such as an ABCD algorithm, but rather on a more global cognitive assessment (Girardi et al, 2004(Girardi et al, , 2006.…”

Section: Introductionmentioning

confidence: 99%

Evidence of a Limited Intra-Individual Diversity of Nevi: Intuitive Perception of Dominant Clusters Is a Crucial Step in the Analysis of Nevi by Dermatologists

Wazaefi

Gaudy‐Marqueste

Avril

et al. 2013

Journal of Investigative Dermatology

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Although nevi are highly polymorphous, it has been suggested that each individual is characterized by only a few dominant patterns of nevi. Therefore, a nevus that does not fit in with these patterns, the "ugly duckling" nevus, is suspicious. Our objective was to study the intra-individual diversity of nevi, using human ability to build "perceived similarity clusters" (PSCs). Nine dermatologists had to cluster all the nevi of 80 patients into PSCs, at the clinical scale (CS) and at the dermoscopic scale (DS) (subset of 30 patients). Nine novices did the same in a subset of 11 patients. The experts identified a mean of 2.8 PSCs/patient at CS. Concordance was higher between experts than between novices at CS and at DS. Despite a trend for more PSCs at DS than at CS, the number of nevus patterns per patient remained low, regardless of the number of nevi. Inter-expert concordance permits a consensus representation of nevus diversity in each individual. Nevus diversity is limited in each patient and constitutes an individual reference system, which we can intuitively perceive. This reference is probably crucial for nevus analysis and melanoma detection and opens perspectives for computer-aided diagnostics.

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What features do patients notice that help to distinguish between benign pigmented lesions and melanomas?: the ABCD(E) rule versus the seven-point checklist

Liu¹,

Hill

Gibbs

et al. 2005

Melanoma Research

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The ABCD(E) rule and the seven-point checklist are diagnostic aids that have proven to be useful in the hands of physicians; however, little is known of their value to patients with respect to aiding self-detection. The objective of this study was to investigate features that patients notice when identifying melanomas and to explore how well these features correspond to the ABCD(E) rule and the seven-point checklist. A retrospective, modified, case-control study involving patient interviews was performed. All interviews were conducted through the private consulting rooms of a Melbourne dermatologist (JWK) and a Newcastle plastic surgeon (CH) prior to the result of pathology being known to the patients and the interviewers. Sixty-seven patients with benign pigmented skin lesions and 46 patients with melanomas were included. Using a logistic regression model, the change in size/new lesion and change in colour (major criteria, seven-point checklist) were most useful in differentiating between melanomas and benign pigmented lesions in the hands of patients [odds ratio (OR), 4.74; 95% confidence interval (CI), 1.85-12.19; P=0.001; OR, 4.27; 95% CI, 1.62-11.26; P=0.003, respectively). The ABCD(E) rule failed to discriminate between melanoma and other benign pigmented skin lesions. It can be concluded that, of the patients' observations, changes in size or colour were most important in distinguishing between benign pigmented lesions and melanomas. Such features therefore deserve emphasis in public education campaigns. Medical professionals should also remember to seek a history of change in assessing pigmented skin lesions.

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21 Randomized assessment of strategies for self-detection in the general population: failure of ABCD, success of cognitive approach

Cited by 3 publications

References 0 publications

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

Evidence of a Limited Intra-Individual Diversity of Nevi: Intuitive Perception of Dominant Clusters Is a Crucial Step in the Analysis of Nevi by Dermatologists

What features do patients notice that help to distinguish between benign pigmented lesions and melanomas?: the ABCD(E) rule versus the seven-point checklist

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