Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
[HTML][HTML] Self supervised contrastive learning for digital histopathology
O Ciga, T Xu, AL Martel - Machine Learning with Applications, 2022 - Elsevier
Unsupervised learning has been a long-standing goal of machine learning and is especially
important for medical image analysis, where the learning can compensate for the scarcity of …
important for medical image analysis, where the learning can compensate for the scarcity of …
Wilds: A benchmark of in-the-wild distribution shifts
Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
Mitosis domain generalization in histopathology images—the MIDOG challenge
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue
slides that exhibit similar but not identical color appearance. Due to this color shift between …
slides that exhibit similar but not identical color appearance. Due to this color shift between …
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 …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Emerging role of deep learning‐based artificial intelligence in tumor pathology
The development of digital pathology and progression of state‐of‐the‐art algorithms for
computer vision have led to increasing interest in the use of artificial intelligence (AI) …
computer vision have led to increasing interest in the use of artificial intelligence (AI) …