Big data in basic and translational cancer research
Historically, the primary focus of cancer research has been molecular and clinical studies of
a few essential pathways and genes. Recent years have seen the rapid accumulation of …
a few essential pathways and genes. Recent years have seen the rapid accumulation of …
Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects
Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to
bring medicine from the era of 'sick-care'to the era of healthcare and prevention. The …
bring medicine from the era of 'sick-care'to the era of healthcare and prevention. The …
Monai label: A framework for ai-assisted interactive labeling of 3d medical images
The lack of annotated datasets is a major bottleneck for training new task-specific
supervised machine learning models, considering that manual annotation is extremely …
supervised machine learning models, considering that manual annotation is extremely …
Foundation model for cancer imaging biomarkers
Foundation models in deep learning are characterized by a single large-scale model trained
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …
The BioImage archive–building a home for life-sciences microscopy data
M Hartley, GJ Kleywegt, A Patwardhan… - Journal of Molecular …, 2022 - Elsevier
Despite the huge impact of data resources in genomics and structural biology, until now
there has been no central archive for biological data for all imaging modalities. The …
there has been no central archive for biological data for all imaging modalities. The …
National cancer institute imaging data commons: toward transparency, reproducibility, and scalability in imaging artificial intelligence
The remarkable advances of artificial intelligence (AI) technology are revolutionizing
established approaches to the acquisition, interpretation, and analysis of biomedical …
established approaches to the acquisition, interpretation, and analysis of biomedical …
MITI minimum information guidelines for highly multiplexed tissue images
The imminent release of tissue atlases combining multichannel microscopy with single-cell
sequencing and other omics data from normal and diseased specimens creates an urgent …
sequencing and other omics data from normal and diseased specimens creates an urgent …
Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge
Deep learning has shown great potential to automate abdominal organ segmentation and
quantification. However, most existing algorithms rely on expert annotations and do not have …
quantification. However, most existing algorithms rely on expert annotations and do not have …
Building flexible, scalable, and machine learning-ready multimodal oncology datasets
The advancements in data acquisition, storage, and processing techniques have resulted in
the rapid growth of heterogeneous medical data. Integrating radiological scans …
the rapid growth of heterogeneous medical data. Integrating radiological scans …
Artificial intelligence for radiation oncology applications using public datasets
Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation
oncology. However, large curated datasets-often involving imaging data and corresponding …
oncology. However, large curated datasets-often involving imaging data and corresponding …