An ensemble deep learning approach to detect tumors on Mohs micrographic surgery slides

Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathological examination demands significant time, effort, and professionality. The objective of this study is to develop a deep learning model to detect basal cell carcinoma (BCC) and artifacts on Mohs slides. A total of 731 Mohs slides from 51 patients with BCCs were used in this study, with 91 containing tumor and 640 without tumor which was defined as non-tumor. The dataset was employed to train U-Net based models that segment tumor and non-tumor regions on the slides. The segmented patches were classified as tumor, or non-tumor to produce predictions for whole slide images (WSIs). For the segmentation phase, the deep learning model success was measured using a Dice score with 0.70 and 0.67 value, area under the curve (AUC) score with 0.98 and 0.96 for tumor and non-tumor, respectively. For the tumor classification, an AUC of 0.98 for patch-based detection, and AUC of 0.91 for slide-based detection was obtained on the test dataset. We present an AI system that can detect tumors and non-tumors in Mohs slides with high success. Deep learning can aid Mohs surgeons and dermatopathologists in making more accurate decisions.

DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 38 Subclasses

Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 38 subclasses of skin lesions collected in Turkiye which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution photos and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with 5 super classes, 15 main classes, 38 subclasses and its 12,345 high-resolution dermatoscopic images.

Automated Onychomycosis Detection Using Deep Neural Networks

Clinical dermatology, still relies heavily on manual introspection of fungi within a Potassium Hydroxide (KOH) solution using a brightfield microscope. However, this method takes a long time, is based on the experience of the clinician, and has a low accuracy. With the increase of neural network applications in the field of clinical microscopy it is now possible to automate such manual processes increasing both efficiency and accuracy. This study presents a deep neural network structure that enables the rapid solutions for these problems and can perform automatic fungi detection in grayscale images without colorants. Microscopic images of 81 fungi and 235 ceratine were collected. Then, smaller patches were extracted containing 2062 fungi and 2142 ceratine. In order to detect fungus and ceratine, two models were created one of which was a custom neural network and the other was based on the VGG16 architecture. The developed custom model had 99.84% accuracy, and an area under the curve (AUC) value of 1.00, while the VGG16 model had 98.89% accuracy and an AUC value of 0.99. However, average accuracy and AUC value of clinicians is 72.8% and 0.87 respectively. This deep learning model allows the development of an automated system that can detect fungi within microscopic images.