AI service — AI Medical · Radiology
Breast cancer screening
Score screening mammograms for malignancy and estimate one-to-five-year risk from the images themselves.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Screening programmes are volume businesses with a reader shortage. These models return a malignancy probability per breast, or a multi-year risk score derived from the mammogram rather than from a questionnaire — which is what allows screening intervals to be set by risk instead of by age alone.
Studies arrive as four-view digital mammograms and come back as scored output per breast or per patient. Weights for one of these models are released on request by the producing lab, which we handle as part of the engagement.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the breast cancer screening pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Reader support — Provide a second score on every screening study for a hybrid read.
- Risk-stratified intervals — Use image-derived risk to set follow-up intervals across a programme.
- Programme audit — Re-score an existing screening archive to measure programme performance.
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