Dermopsy - Deep Learning Based Pas Stained Slide Analysis And Noninvasive Confocal Image Staining

Abstract

Skin disorders like skin cancer and fungal illnesses of the skin result in severe health complications and cause many deaths annually. Today’s methods of diagnosis prove to be time-consuming and, in many cases, invasive thus causing a lot of discomfort to the patient and the doctor is unable to proceed with the necessary action as soon as one would wish. Studies show that early cancer detection increases the survival rate from 32% to 98%. Therefore, this study set out to use deep learning methods to diagnose skin diseases. The project’s objectives are to develop deep learning models for virtual staining of Reflectance Confocal Microscopic (RCM) images, and to segment the elements of fungi from Periodic acid–Schiff (PAS) stained slides. These models are deployed on a cloudbased web platform to aid dermatopathologists in performing conclusive diagnoses. This methodology enables precise identification and differentiation of various tissue components, assisting in diagnosing complex skin conditions and diseases. Using deep learning techniques, we have achieved impressive results. Our Transformer based Segmentation model has a Mean IoU of 85.2% and it accurately distinguishes between 5 separate classes. Moreover, fungal elements are also highlighted with great precision ensuring valid diagnosis. Furthermore, several dermatopathologists assessed our virtual staining model based on factors like color, sharpness, contrast, brightness, etc. They found that it met medical criteria for identifying nuclei components for diagnosis. These outstanding outcomes demonstrate the efficacy and dependability of our cloud-based solution in giving dermatologists accurate and consistent histopathology analysis. This project has been developed in partnership with NIDI Skin, a well-known dermatology organization with headquarters in the US. We have developed a strong and effective application that completely changes the histopathological analysis in dermatology by fusing the knowledge of experienced dermatopathologists with our technological innovations. In conclusion, compared to conventional staining methods, our cloud-based software, which is driven by deep learning algorithms, offers a reliable, rapid, and affordable alternative. With its sophisticated segmentation capabilities and virtual staining, it provides dermatopathologists with comprehensive insights into skin tissues, enabling them to diagnose patients accurately and decide the best course of therapy