Data-efficient and weakly supervised computational pathology on whole-slide images
Deep-learning methods for computational pathology require either manual annotation of
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …
gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and …
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
G Campanella, MG Hanna, L Geneslaw, A Miraflor… - Nature medicine, 2019 - nature.com
The development of decision support systems for pathology and their deployment in clinical
practice have been hindered by the need for large manually annotated datasets. To …
practice have been hindered by the need for large manually annotated datasets. To …
An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
Deep learning for digital pathology is hindered by the extremely high spatial resolution of
whole-slide images (WSIs). Most studies have employed patch-based methods, which often …
whole-slide images (WSIs). Most studies have employed patch-based methods, which often …
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
Artificial intelligence (AI) can extract visual information from histopathological slides and
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …
yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of …
Differentiable zooming for multiple instance learning on whole-slide images
Abstract Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …
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 …
A multi-resolution model for histopathology image classification and localization with multiple instance learning
Large numbers of histopathological images have been digitized into high resolution whole
slide images, opening opportunities in developing computational image analysis tools to …
slide images, opening opportunities in developing computational image analysis tools to …
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 …
Multiple instance learning with center embeddings for histopathology classification
P Chikontwe, M Kim, SJ Nam, H Go… - Medical Image Computing …, 2020 - Springer
Histopathology image analysis plays an important role in the treatment and diagnosis of
cancer. However, analysis of whole slide images (WSI) with deep learning is challenging …
cancer. However, analysis of whole slide images (WSI) with deep learning is challenging …
Semi-supervised histology classification using deep multiple instance learning and contrastive predictive coding
Convolutional neural networks can be trained to perform histology slide classification using
weak annotations with multiple instance learning (MIL). However, given the paucity of …
weak annotations with multiple instance learning (MIL). However, given the paucity of …