The field of pathology and microscopy is witnessing significant advancements with the integration of deep learning techniques. Recent developments are focused on improving the accuracy and efficiency of image analysis, particularly in the classification of histopathology images and the detection of specific features such as mitosis and immunohistochemical stains.

Noteworthy papers in this area include ForamDeepSlice, which presents a high-accuracy deep learning framework for foraminifera species classification, and OnSight Pathology, a real-time platform-agnostic computational pathology companion for histopathology. The prostate biopsy whole slide image dataset from an underrepresented Middle Eastern population is also a valuable contribution, enabling the development and validation of pathology AI models across globally diverse populations. Dataset creation for supervised deep learning-based analysis of microscopic images provides a comprehensive guide to the critical steps in dataset creation, including image acquisition, selection of annotation software, and annotation creation.