Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
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 …
Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning
We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …
very high resolutions and usually lack localized annotations. WSI classification can be cast …
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 …
Causability and explainability of artificial intelligence in medicine
A Holzinger, G Langs, H Denk… - … Reviews: Data Mining …, 2019 - Wiley Online Library
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the
problem of explainability is as old as AI itself and classic AI represented comprehensible …
problem of explainability is as old as AI itself and classic AI represented comprehensible …
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 …
Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning
Automated Screening of COVID-19 from chest CT is of emergency and importance during
the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 …
the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Weakly supervised deep learning for whole slide lung cancer image analysis
Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …
and precise diagnosis is quite critical for the subsequent therapeutic treatment of patients …