Search Results for author:
Found 5 papers, 4 papers with code
Structured State Space Models for Multiple Instance Learning in Digital Pathology
In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified.
Artifact Removal in Histopathology Images
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis.
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field.
ASNI: Adaptive Structured Noise Injection for shallow and deep neural networks
Dropout is a regularisation technique in neural network training where unit activations are randomly set to zero with a given probability \emph{independently}.
