Key points are not available for this paper at this time.
Abstract Background The broad histomorphological spectrum of melanocytic pathologies requires large data sets to develop accurate and generalisable deep learning (DL)‐based diagnostic pathology classifiers. Weakly supervised DL promotes utilisation of larger training data sets compared to fully supervised (patch annotation) approaches. Objectives To evaluate weakly supervised DL image classifiers for discriminating melanomas from naevi on haematoxylin and eosin (H melanoma median age: 71.5 years), with more balanced sex distribution (males: 48.8%, melanoma male subgroup: 64.2%). The most frequent histological subtypes of naevi and melanomas were dysplastic compound ( n = 99, 38.1%) and superficial spreading ( n = 124, 47.7%), respectively. Average AUC (±1 SD) for Trans‐MIL, CLAM and DTFD‐MIL across test groups were 0.9952 ± 0.006, 0.9925 ± 0.0052 and 0.9708 ± 0.0328, at 20× magnification, respectively. Performance of the models varied according to the magnification used. Heatmaps from the two best performing models, Trans‐MIL and CLAM, generally indicated attention on appropriate tissue regions for interpretation. Conclusions Weakly supervised DL on pathological slides of common mucocutaneous melanocytic tumours provides highly accurate diagnostic value for discrimination of melanomas and naevi. External validation and further assessment on less frequently occurring histologic subtypes and borderline cases using this method is required.
Maher et al. (Sat,) studied this question.