Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential
M Gadermayr, M Tschuchnig - Computerized Medical Imaging and …, 2024 - Elsevier
Digital whole slides images contain an enormous amount of information providing a strong
motivation for the development of automated image analysis tools. Particularly deep neural …
motivation for the development of automated image analysis tools. Particularly deep neural …
Visual language pretrained multiple instance zero-shot transfer for histopathology images
Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …
training new language-aware image encoders or augmenting existing pretrained models …
Morphological prototyping for unsupervised slide representation learning in computational pathology
Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
Co-pilot: Dynamic top-down point cloud with conditional neighborhood aggregation for multi-gigapixel histopathology image representation
Predicting survival rates based on multi-gigapixel histopathology images is one of the most
challenging tasks in digital pathology. Due to the computational complexities, Multiple …
challenging tasks in digital pathology. Due to the computational complexities, Multiple …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Weakly supervised joint whole-slide segmentation and classification in prostate cancer
The identification and segmentation of histological regions of interest can provide significant
support to pathologists in their diagnostic tasks. However, segmentation methods are …
support to pathologists in their diagnostic tasks. However, segmentation methods are …
Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network
M Liang, Q Chen, B Li, L Wang, Y Wang… - Computer methods and …, 2023 - Elsevier
Abstract Background and Objective Whole slide image (WSI) classification and lesion
localization within giga-pixel slide are challenging tasks in computational pathology that …
localization within giga-pixel slide are challenging tasks in computational pathology that …
Sac-net: enhancing spatiotemporal aggregation in cervical histological image classification via label-efficient weakly supervised learning
Cervical cancer is the fourth most common cancer in women and its subtyping requires
examining histopathological slides or digital images, such as whole slide images (WSIs) …
examining histopathological slides or digital images, such as whole slide images (WSIs) …
MG-Trans: Multi-scale Graph Transformer with Information Bottleneck for Whole Slide Image Classification
Multiple instance learning (MIL)-based methods have become the mainstream for
processing the megapixel-sized whole slide image (WSI) with pyramid structure in the field …
processing the megapixel-sized whole slide image (WSI) with pyramid structure in the field …