Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
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 …
Harnessing multimodal data integration to advance precision oncology
Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …
insights for patients with cancer. However, most approaches are limited to a single mode of …
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …
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 …
Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study
R Yamashita, J Long, T Longacre, L Peng… - The Lancet …, 2021 - thelancet.com
Background Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical
decision making, as it identifies patients with differential treatment response and prognosis …
decision making, as it identifies patients with differential treatment response and prognosis …
Deficient mismatch repair/microsatellite unstable colorectal cancer: Diagnosis, prognosis and treatment
Microsatellite unstable (MSI) colorectal cancers (CRCs) are due to DNA mismatch repair
(MMR) deficiency and occurs in15% of non-metastatic diseases and 5% in the metastatic …
(MMR) deficiency and occurs in15% of non-metastatic diseases and 5% in the metastatic …
[HTML][HTML] Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
D Nam, J Chapiro, V Paradis, TP Seraphin, JN Kather - Jhep Reports, 2022 - Elsevier
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …
metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative …