Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Bevan, Peter J.; Atapour-Abarghouei, Amir

doi:10.48550/arxiv.2109.09818

Cited by 3 publications

(2 citation statements)

References 30 publications

(127 reference statements)

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“…The situation is even more complex in medical image analysis for specialities such as radiology (National Lung Screening Trial, MIMIC-CXR-JPG [42], CheXpert [43]) or dermatology (Melanoma detection for skin cancer, HAM10000 database [44]), where biased datasets are provided for medical applications. Indeed, under-represented populations in some datasets lead to a critical drop in accuracy, for instance in skin cancer detection, as in [45,46], or for general research in medicine [47] and references therein.…”

Section: Improperly Sampled Training Datamentioning

confidence: 99%

Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning

Risser,

Picard,

Hervier

et al. 2023

Algorithms

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The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applications, where high-dimensional inputs are used. This issue has, however, been mostly left out of the spotlight in the machine learning literature. Contrary to societal applications, where a set of potentially sensitive variables, such as gender or race, can be defined by common sense or by regulations to draw attention to potential risks, the sensitive variables are often unsuspected in industrial and safety-critical applications. In addition, these unsuspected sensitive variables may be indirectly represented as a latent feature of the input data. For instance, the predictions of an image classifier may be altered by reconstruction artefacts in a small subset of the training images. This raises serious and well-founded concerns about the commercial deployment of AI-based solutions, especially in a context where new regulations address bias issues in AI. The purpose of our paper is, then, to first give a large overview of recent advances in robust machine learning. Then, we propose a new procedure to detect and to treat such unknown biases. As far as we know, no equivalent procedure has been proposed in the literature so far. The procedure is also generic enough to be used in a wide variety of industrial contexts. Its relevance is demonstrated on a set of satellite images used to train a classifier. In this illustration, our technique detects that a subset of the training images has reconstruction faults, leading to systematic prediction errors that would have been unsuspected using conventional cross-validation techniques.

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Section: Improperly Sampled Training Datamentioning

confidence: 99%

Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning

Risser,

Picard,

Hervier

et al. 2023

Algorithms

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“…achieved the best classification results with an AUC of 0.911 and balanced multiclass accuracy of 0.831 on three skin cancer classification tasks of ISIC-2017 by using an ensemble of ResNet-50 networks on normalized images ( 72 ). used ensemble learning with a stacking scheme and obtained the classification results with an accuracy of 0.885 and an AUC of 0.983 in the ISIC-2018 competition ( 73 ). employed two bias removal techniques, “Learning Not to Learn” (LNTL) and “Turning a Blind Eye” (TABE), to alleviate irregularities in model predictions and spurious changes in melanoma images.…”

Section: Dermatological Images and Datasetsmentioning

confidence: 99%

Skin Cancer Classification With Deep Learning: A Systematic Review

Chen

Zeng

et al. 2022

Front. Oncol.

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Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model’s cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers’ convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

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