{"paper":{"title":"Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew P. Bradley, Gustavo Carneiro, Neeraj Dhungel, Zhi Lu","submitted_at":"2016-10-07T12:36:21Z","abstract_excerpt":"In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($\\mu$C). Currently, the most effective methods are based on three steps: 1) detection of individual $\\mu$C candidates, 2) clustering of individual $\\mu$C candidates, and 3) classification of $\\mu$C clusters. Where the second step is motivated both to reduce the number of false positive detections from the first step and on the evidence that malignancy depends on a relatively large number of $\\mu$C detections within a certain area. In this paper, we propose a no"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02251","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}