{"ID":2866192,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21561","arxiv_id":"2509.21561","title":"Unsupervised Defect Detection for Surgical Instruments","abstract":"Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existing automated defect detection methods, typically trained on natural/industrial images, fail to transfer effectively to the surgical domain. We demonstrate that simply applying or fine-tuning these approaches leads to issues: false positive detections arising from textured backgrounds, poor sensitivity to small, subtle defects, and inadequate capture of instrument-specific features due to domain shift. To address these challenges, we propose a versatile method that adapts unsupervised defect detection methods specifically for surgical instruments. By integrating background masking, a patch-based analysis strategy, and efficient domain adaptation, our method overcomes these limitations, enabling the reliable detection of fine-grained defects in surgical instrument imagery.","short_abstract":"Ensuring the safety of surgical instruments requires reliable detection of visual defects. However, manual inspection is prone to error, and existing automated defect detection methods, typically trained on natural/industrial images, fail to transfer effectively to the surgical domain. We demonstrate that simply applyi...","url_abs":"https://arxiv.org/abs/2509.21561","url_pdf":"https://arxiv.org/pdf/2509.21561v2","authors":"[\"Joseph Huang\",\"Yichi Zhang\",\"Jingxi Yu\",\"Wei Chen\",\"Seunghyun Hwang\",\"Qiang Qiu\",\"Amy R. Reibman\",\"Edward J. Delp\",\"Fengqing Zhu\"]","published":"2025-09-25T20:40:52Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}