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In the era of artificial intelligence and machine learning, computer-aided diagnostic frameworks are data-hungry and require large amounts of annotated data to automate the disease diagnosis procedure. Moreover, to enhance the performance and accuracy of disease diagnosis, procedures need to be automated to ensure timely and accurate diagnosis. We are providing a whole slide image repository comprising unstained skin biopsy images acquired using a brightfield microscope, along with Hematoxylin and Eosin chemically and virtually stained image samples, to virtualize the staining procedure and enhance the efficiency of the disease diagnosis pipeline. The dataset was utilized to train a Dual Contrastive GAN to generate virtually stained image samples. The trained model achieved an FID score of 80.47 between virtually stained and chemically stained image samples, indicating a high correlation of content between synthesized and original images. In contrast, FID scores of 342.01 and 320.40 were observed between unstained images and virtually stained slides, and between unstained images and chemically stained images, respectively, indicating less similarity in content.

Skin diseases have a significant impact on the socio-economic landscape as they affect not only the medical health of the patient but also their psychological well-being. Moreover, as the majority of individuals suffering from skin diseases are over the age of 60, these individuals have to also cope with the stress associated to age-related conditions such as diabetes, high blood pressure, and cardiac diseases. To alleviate this burden, it is essential to identify skin diseases at an early stage, which can help prevent disease progression. With the advent of Artificial Intelligence (AI) and technology, the use of automated disease diagnosis systems has increased significantly. These systems assist medical specialists by reducing diagnosis time and accelerating the entire diagnostic process. However, deep learning models require substantial amounts of data for training. In histopathology, brightfield microscopy is the most widely used imaging modality for identifying diseases through the examination of underlying structures. We are publishing a dataset comprising 38 whole-slide Hematoxylin and Eosin-stained images along with their masks. These images were grouped into 12 classes including tissues, skin cancer, and skin layers. We have also validated the dataset using SegFormer, which resulted in an overall accuracy of 0.875.