אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Digital pathology has emerged as a transformative field, enabling automated imaging and computational analysis of thin tissue biopsy slices or body fluids. These samples are typically stained to enhance contrast in biological structures and reveal their morphology under microscopic magnification. Digitally scanning these stained samples produces gigapixel-scale images, known as whole slide images (WSIs), which pose significant challenges for computational analysis using deep learning techniques. Variability in staining protocols and scanning devices across medical centers introduces inconsistencies in WSIs, while their large size imposes substantial computational constraints. Furthermore, most slides are annotated such that the pathologist's prognosis is provided as a holistic textual report at the patient level, rather than pinpointing the specific diagnostic regions in the image that indicate the medical estimate.

To address these challenges, we propose an end-to-end approach for WSI analysis. Our method involves self-supervised pretraining on patches extracted from a large collection of WSIs to learn generic and transferable feature representations without requiring manual annotations. Subsequently, we employ a Transformer-based architecture trained in a weakly supervised manner to effectively aggregate spatial and contextual information across image regions, producing accurate slide-level predictions.

We evaluated our proposed method on multiple WSI datasets for various clinically relevant molecular profiling tasks, such as determining hormonal receptor status in invasive breast cancer, demonstrating its effectiveness and generalizability. Leveraging recent advances in computer vision, our approach addresses the challenges of WSI analysis, providing insights for processing large and complex images. By enhancing robustness and predictive accuracy in various clinical tasks within computational histopathology, our approach has the potential to improve clinical decision-making, ultimately contributing to better patient outcomes.