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 …
Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic
potential and poor prognosis, and has limited treatment options. The current standard of …
potential and poor prognosis, and has limited treatment options. The current standard of …
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 …
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 …
Deep learning in cancer pathology: a new generation of clinical biomarkers
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …
However, the growing number of these complex biomarkers tends to increase the cost and …
Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma
J Calderaro, TP Seraphin, T Luedde, TG Simon - Journal of hepatology, 2022 - Elsevier
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and
the third-leading cause of cancer-related death worldwide, with incidence and mortality rates …
the third-leading cause of cancer-related death worldwide, with incidence and mortality rates …
Designing deep learning studies in cancer diagnostics
A Kleppe, OJ Skrede, S De Raedt, K Liestøl… - Nature Reviews …, 2021 - nature.com
The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …
and systems are frequently claimed to perform comparable with or better than clinicians …
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 …
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 …