Multimodal biomedical AI
The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …
Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …
been successful at capturing image representations but their use has been generally …
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 …
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
J Ogier du Terrail, A Leopold, C Joly, C Béguier… - Nature medicine, 2023 - nature.com
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 …
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 …
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …
emerging that have enabled researchers to access multi-layer information from the genome …
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 …
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
The rapidly emerging field of computational pathology has demonstrated promise in
developing objective prognostic models from histology images. However, most prognostic …
developing objective prognostic models from histology images. However, most prognostic …