An intuitive explanation of dermoscopic structures by digitally reconstructed pathological horizontal top-down view images

Kasuya, Akira; Aoshima, Masahiro; Fukuchi, Kazuki; Shimauchi, Takatoshi; Fujiyama, Toshiharu; Tokura, Y.

doi:10.1038/s41598-019-56522-8

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Furthermore, we analysed the spectral signatures of nevi with different dermoscopic patterns and histopathological types. Melanocytic lesions present dermoscopic patterns and features that correlate to histological morphology 37 . Against this background, we recorded dermoscopic images of different types of melanocytic nevi located on the trunk of a single volunteer (male, 27 years old) and labelled their histopathological type and corresponding pattern together with a board-certified derma-…”

Section: Discussionmentioning

confidence: 99%

Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging

Hetz,

Garcia,

Haggenmüller

et al. 2024

Preprint

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Clinical dermatology necessitates precision and innovation for efficient diagnosis and treatment of various skin conditions. Thispaper introduces the development of a cutting-edge hyperspectral dermatoscope (the Hyperscope) tailored for human skinanalysis. We detail the requirements to such a device and the design considerations, from optical configurations to sensorselection, necessary to capture a wide spectral range with high fidelity. Preliminary results from 15 individuals and 160 recordedskin images demonstrate the potential of the Hyperscope in identifying and characterizing various skin conditions, offeringa promising avenue for non-invasive skin evaluation and a platform for future research in dermatology-related hyperspectralimaging.

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Section: Discussionmentioning

confidence: 99%

Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging

Hetz,

Garcia,

Haggenmüller

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Dermoscopy images are captured with dermoscopy, a type of optical observation tool used to assess the fine details of skin diseases ( 41 ). Clinicians frequently utilize dermoscopy to diagnose benign nevi and malignant melanoma ( 42 ). It serves as a bridge between clinical and pathological aspects, and thus dermoscopy is often referred to as a dermatologist’s stethoscope.…”

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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Can we predict postinflammatory hyperpigmentation after laser treatment based on dermoscopic findings of solar lentigo?

et al. 2023

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An intuitive explanation of dermoscopic structures by digitally reconstructed pathological horizontal top-down view images

Cited by 8 publications

References 16 publications

Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging

Advancing Dermatological Diagnosis: Development of a Hyperspectral Dermatoscope for Enhanced Skin Imaging

Skin Cancer Classification With Deep Learning: A Systematic Review

Can we predict postinflammatory hyperpigmentation after laser treatment based on dermoscopic findings of solar lentigo?

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