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

Indications for Digital Monitoring of Patients With Multiple Nevi: Recommendations from the International Dermoscopy Society

Introduction: In patients with multiple nevi, sequential imaging using total body skin photography (TBSP) coupled with digital dermoscopy (DD) documentation reduces unnecessary excisions and improves the early detection of melanoma. Correct patient selection is essential for optimizing the efficacy of this diagnostic approach. Objectives: The purpose of the study was to identify, via expert consensus, the best indications for TBSP and DD follow-up. Methods: This study was performed on behalf of the International Dermoscopy Society (IDS). We attained consensus by using an e-Delphi methodology. The panel of participants included international experts in dermoscopy. In each Delphi round, experts were asked to select from a list of indications for TBSP and DD. Results: Expert consensus was attained after 3 rounds of Delphi. Participants considered a total nevus count of 60 or more nevi or the presence of a CDKN2A mutation sufficient to refer the patient for digital monitoring.  Patients with more than 40 nevi were only considered an indication in case of personal history of melanoma or red hair and/or a MC1R mutation or history of organ transplantation. Conclusions: Our recommendations support clinicians in choosing appropriate follow-up regimens for patients with multiple nevi and in applying the time-consuming procedure of sequential imaging more efficiently. Further studies and real-life data are needed to confirm the usefulness of this list of indications in clinical practice

Applying a decision support system in clinical practice: Results from melanoma diagnosis

Abstract The work reported in this paper investigates the use of a decision-support tool for the diagnosis of pigmented skin lesions in a real-world clinical trial with 511 patients and 3827 lesion evaluations. We analyzed a number of outcomes of the trial, such as direct comparison of system performance in laboratory and clinical setting, the performance of physicians using the system compared to a control dermatologist without the system, and repeatability of system recommendations. The results show that system performance was significantly less in the real-world setting compared to the laboratory setting (c-index of 0.87 vs. 0.94, p = 0.01). Dermatologists using the system achieved a combined sensitivity of 85% and combined specificity of 95%. We also show that the process of acquiring lesion images using digital dermoscopy devices needs to be standardized before sufficiently high repeatability of measurements can be assured

Clinical and dermoscopic characteristics of congenital and non-congenital nevus-associated melanomas

No specific features of nevus-associated melanoma (NAM) are currently defined

Inflammoskopie: Dermatoskopie bei entz\ufcndlichen, infiltrierenden und infekti\uf6sen Dermatosen : Indikation und standardisierte dermatoskopische Terminologie

Dermatoscopy as a\ua0noninvasive diagnostic tool is not only useful in the differentiation of malignant and benign skin tumors, but is also effective in the diagnosis of inflammatory, infiltrative and infectious dermatoses. As a result, the need for diagnostic punch biopsies in dermatoses could be reduced. Hereby the selection of affected skin areas is essential. The diagnostic accuracy is independent of the skin type. Helpful dermatoscopic features include vessels morphology and distribution, scales colors and distribution, follicular findings, further structures such as colors and morphology as well as specific clues. The dermatoscopic diagnosis is made based on the descriptive approach in clinical routine, teaching and research. In all clinical and dermatoscopic diagnoses that remain unclear, a\ua0punch biopsy with histopathology should be performed. The dermatoscope should be cleaned after every examination according to the guidelines

Seven non-melanoma features to rule out facial melanoma

Facial melanoma is difficult to diagnose and dermatoscopic features are often subtle. Dermatoscopic non-melanoma patterns may have a comparable diagnostic value. In this pilot study, facial lesions were collected retrospectively, resulting in a case set of 339 melanomas and 308 non-melanomas. Lesions were evaluated for the prevalence (> 50% of lesional surface) of 7 dermatoscopic non-melanoma features: scales, white follicles, erythema/reticular vessels, reticular and/or curved lines/fingerprints, structureless brown colour, sharp demarcation, and classic criteria of seborrhoeic keratosis. Melanomas had a lower number of non-melanoma patterns (p < 0.001). Scoring a lesion suspicious when no prevalent non-melanoma pattern is found resulted in a sensitivity of 88.5% and a specificity of 66.9% for the diagnosis of melanoma. Specificity was higher for solar lentigo (78.8%) and seborrhoeic keratosis (74.3%) and lower for actinic keratosis (61.4%) and lichenoid keratosis (25.6%). Evaluation of prevalent non-melanoma patterns can provide slightly lower sensitivity and higher specificity in detecting facial melanoma compared with already known malignant features

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