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 …
Tumour-infiltrating lymphocytes: from prognosis to treatment selection
K Brummel, AL Eerkens, M de Bruyn… - British Journal of …, 2023 - nature.com
Tumour-infiltrating lymphocytes (TILs) are considered crucial in anti-tumour immunity.
Accordingly, the presence of TILs contains prognostic and predictive value. In 2011, we …
Accordingly, the presence of TILs contains prognostic and predictive value. In 2011, we …
Swarm learning for decentralized artificial intelligence in cancer histopathology
Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …
routine histopathology slides. However, training robust AI systems requires large datasets …
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 …
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …
pathology slides in colorectal cancer (CRC). However, current approaches rely on …
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 …
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 …
Multimodal deep learning for integrating chest radiographs and clinical parameters: a case for transformers
Background Clinicians consider both imaging and nonimaging data when diagnosing
diseases; however, current machine learning approaches primarily consider data from a …
diseases; however, current machine learning approaches primarily consider data from a …
Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images
Current diagnosis of glioma types requires combining both histological features and
molecular characteristics, which is an expensive and time-consuming procedure …
molecular characteristics, which is an expensive and time-consuming procedure …