radiology
No description yet: the sentence for this tag has not been written. 8 records carry it: 7 technologies, 1 person.
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| Cancers | Other tags | ||||
|---|---|---|---|---|---|
Aidoc CARE (clinical radiology foundation model) Aidoc CARE is one radiology foundation model, pretrained on CT scans without labels, whose task-specific heads have each been FDA-cleared to flag urgent findings in emergency scans so radiologists read those first. Its oncology relevance is indirect, catching incidental masses; the regulatory evidence covers triage, not diagnostic accuracy for tumours. | none | foundation-model | |||
CT-FM (whole-body CT foundation model) A model pretrained on 148,000 CT scans to segment organs and triage findings. | none | foundation-model | |||
Denise R. Aberle Professor of Radiology and Bioengineering, UCLA · UCLA Jonsson Comprehensive Cancer Center Principal investigator of the National Lung Screening Trial, which first proved CT screening saves lives. | Non-small-cell lung cancer | none | screening, lung | ||
MedSAM / SAM-Med3D (segment anything for medicine) Adaptations of Meta's Segment Anything model that outline tumours and organs on any scan with a click. | none | foundation-model | |||
Merlin (Stanford abdominal CT vision-language model) Merlin is a model trained on 15,000 CT scans with their reports that can find and describe hundreds of findings. | none | foundation-model | |||
Mirai (MIT breast cancer risk from mammograms) Reads a mammogram to estimate five-year breast cancer risk, consistently across races and devices. | HR-positive / HER2-negative breast cancer, Triple-negative breast cancer | risk-model | |||
RadFM (generalist radiology foundation model) An open generalist model that answers questions about 2D and 3D scans. | none | foundation-model | |||
Sybil (MIT/MGH lung cancer risk from CT) Predicts a person's six-year lung cancer risk from one low-dose CT, even when no nodule is visible. | Non-small-cell lung cancer | risk-model |