The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy

Acquired perforating dermatosis: A clinical and dermatoscopic correlation

Acquired Perforating Dermatosis (APD) is a perforating disease characterized by transepidermal elimination of dermal material [1,2]. This disease usually develops in adulthood. APD has been reported to occur in association with various diseases, but is most commonly associated with dialysis-dependent chronic renal failure (CRF) or diabetes mellitus (DM) [1,2,3,4]. Morton et al found that APD occurs in up to 10% of patients undergoing hemodialysis [5]. Additionally, Saray et al found that sixteen of twenty-two cases with APD were associated with CRF [3]

A reinforcement learning model for AI-based decision support in skin cancer

: We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms

Clinical evolution of Spitz nevi

Nevus Spitz is a benign melanocytic proliferation, first described in 1948 by Sophie Spitz as a childhood melanoma. Initially, it was described as an erythematous papule or node, but further studies of the Spitz nevus proved that in 71-92% cases it is a pigmented formation. This pigmentation is often quite intense due to the rapid growth of the formation, which leads to the need for differential diagnosis with skin melanoma. After all, dermatoscopy can be used for this purpose and, when applying this research method, typically a pattern of an exploding star formed by streaks of linear pigmentation and symmetrically located pigment globules placed in the peripheral zone can be revealed. In case of non-pigmented Spitz nevus, spot vessels and reticular depigmentation are visualized. Both pigmented and non-pigmented forms of Spitz nevus in the process of evolution can regress partially or completely. Several clinical cases of different types of spitzoids, both typical and atypical, based on the non-clinical, dermatoscopic and histological diversity of the Spitz nevi, have been demonstrated in the article. Their macroscopic and dermatoscopic features as well as probable signs of dynamic changes are indicated in order to facilitate their recognition by other specialists

Black salve in a nutshell

Background: Black salve is an alternative therapy increasingly chosen by patients to self-manage their skin lesions. It is promoted as an effective, safe and natural skin cancer treatment, but such claims are not evidence-based, and serious complications have been reported. The sale of black salve in Australia is illegal. Objective: The aim of this article is to educate general practitioners (GPs) about black salve, enabling informed discussion with patients considering using black salve. An overview of the scientific literature is presented. Discussion: Case reports have described significant morbidity and even mortality associated with the use of black salve. Despite this, black salve is readily accessible to the public online; a simple internet search yields multiple links to websites endorsing black salve as an effective natural skin cancer remedy. As GPs are often called on in the initial presentation of skin complaints, they are well positioned to ask patients about their use of black salve and educate them about its risks