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 …
Artificial intelligence to identify genetic alterations in conventional histopathology
Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer
S Foersch, C Glasner, AC Woerl, M Eckstein… - Nature medicine, 2023 - nature.com
Although it has long been known that the immune cell composition has a strong prognostic
and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore …
and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore …
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 …
Adversarial attacks and adversarial robustness in computational pathology
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis
and providing biomarkers directly from routine pathology slides. However, AI applications …
and providing biomarkers directly from routine pathology slides. However, AI applications …
Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …
Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review
Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …
common in men, with an increasing incidence. Pathology diagnosis complemented with …
Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using …
Background When endoscopically resected specimens of early colorectal cancer (CRC)
show high-risk features, surgery should be performed based on current guidelines because …
show high-risk features, surgery should be performed based on current guidelines because …
One label is all you need: Interpretable AI-enhanced histopathology for oncology
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …
benefit oncology through interpretable methods that require only one overall label per …
A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application
M Mokoatle, V Marivate, D Mapiye, R Bornman… - BMC …, 2023 - Springer
Background Using visual, biological, and electronic health records data as the sole input
source, pretrained convolutional neural networks and conventional machine learning …
source, pretrained convolutional neural networks and conventional machine learning …