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Low-dose CTRisk StratificationMultiple Instance Learning

Opportunistic Breast Cancer Risk Stratification from Low-Dose Chest CT Using Multiple Instance Learning

German Conference on Medical Image Computing (BVM) / 2026 / conference

Ni Y, Bhandary Panambur A, Liu C, Nguyen T-T, Bayer S, Maier A

conference / 2026

Medical AI visual summary

Low-dose CTRisk StratificationMultiple Instance Learning
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Scientific summary

This BVM 2026 work investigates opportunistic breast cancer risk stratification from low-dose chest CT using multiple-instance learning.

Abstract

This BVM 2026 poster studies opportunistic breast cancer risk stratification from low-dose chest CT using multiple instance learning.

Why it matters

Opportunistic risk assessment can extract preventive value from imaging acquired for other clinical indications.

Contribution

The public BVM program verifies the title, authors, and poster-session placement.

Method overview

The title indicates a multiple instance learning setup for risk stratification from low-dose chest CT.

Key findings

  • Accepted as poster P55 in the BVM 2026 program.
  • TODO: Add metrics after the abstract or paper is publicly available.

Citation

Ni Y, Bhandary Panambur A, Liu C, Nguyen T-T, Bayer S, Maier A. (2026). Opportunistic Breast Cancer Risk Stratification from Low-Dose Chest CT Using Multiple Instance Learning. German Conference on Medical Image Computing (BVM).

TODO: Add BibTeX.