Publications by authors named "Walt Williams"
Article Synopsis
- The study emphasizes the importance of quantitative evaluation of tissue images for computational pathology, highlighting the challenges posed by high-resolution whole-slide images and their variable features.
- Efforts to utilize pretrained image encoders through transfer learning and self-supervised learning are mentioned, yet there is a gap in extensive evaluation across various tissue types.
- The introduction of UNI, a self-supervised model trained on over 100 million images from diverse tissue types, showcases its ability to exceed previous models and tackle complex pathology tasks, paving the way for effective AI applications in diagnostic workflows.
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale.
View Article and Find Full Text PDF