Speakers
Description
The application of deep learning in gigapixel whole slide image analysis has shown promising results in terms of accuracy and efficiency compared to traditional image analysis techniques. Transformer based models as the current state-of-the-art algorithms are capable of identifying and classifying various structures and patterns within the tissue, providing insights into the underlying pathology and helping in the diagnosis and treatment of diseases. However analyzing pathological whole slide images with vision transformers requires specialized hardware with high computational power, high-speed memory and interconnects. High-performance GPUs and TPUs are commonly used for this purpose, as they are designed specifically for processing large amounts of data in parallel. In this presentation we demonstrate an efficient pipeline for image preprocessing and application of deep learning models at TB scale for digital histopathology.
