[HTML][HTML] Recent advances of deep learning for computational histopathology: principles and applications
Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
[HTML][HTML] Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review
The rapid development of diagnostic technologies in healthcare is leading to higher
requirements for physicians to handle and integrate the heterogeneous, yet complementary …
requirements for physicians to handle and integrate the heterogeneous, yet complementary …
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …
been successful at capturing image representations but their use has been generally …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
Multimodal co-attention transformer for survival prediction in gigapixel whole slide images
Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …
in computational pathology that involves modeling complex interactions within the tumor …
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 …
Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks
Cancer prognostication is a challenging task in computational pathology that requires
context-aware representations of histology features to adequately infer patient survival …
context-aware representations of histology features to adequately infer patient survival …
Interventional bag multi-instance learning on whole-slide pathological images
Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …
Cluster-to-conquer: A framework for end-to-end multi-instance learning for whole slide image classification
Y Sharma, A Shrivastava, L Ehsan… - … Imaging with Deep …, 2021 - proceedings.mlr.press
In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use
of deep learning-based computer vision techniques for automated disease diagnosis …
of deep learning-based computer vision techniques for automated disease diagnosis …
[HTML][HTML] Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study
H Yang, L Chen, Z Cheng, M Yang, J Wang, C Lin… - BMC medicine, 2021 - Springer
Background Targeted therapy and immunotherapy put forward higher demands for accurate
lung cancer classification, as well as benign versus malignant disease discrimination. Digital …
lung cancer classification, as well as benign versus malignant disease discrimination. Digital …