The paper presents a distribution‑free risk‑control framework that provides organ‑specific recall guarantees for frozen multi‑organ CT segmentation models. It calibrates per‑organ thresholds for an AMOS‑trained nnU‑Net, audits its transfer to RAOS, and estimates local re‑certification costs using case‑level voxel false‑negative rates. The study compares Risk‑Controlling Prediction Sets (RCPS) and Conformal Risk Control (CRC), noting that RCPS offers high‑probability control of population‑mean risk while CRC provides weaker expectation control, and evaluates the effectiveness of the Waudby–Smith–Ramdas betting bound versus Hoeffding–Bentkus bounds for re‑certification.
"whyItMatters":"The work demonstrates how to maintain organ‑level recall guarantees when deploying segmentation models across different clinical domains, highlighting the trade‑offs between threshold conservatism and re‑certification effort."
By Souraj Adhikary, Negar Chabi, Andre Mastmeyer
The paper introduces a distribution‑free risk control method that provides organ‑specific recall guarantees for frozen segmentation models. It calibrates per‑organ thresholds on an AMOS‑trained nnU‑Net, audits transfer to RAOS, and estimates local re‑certification cost using case‑level voxel false‑negative rates. The study compares Risk‑Controlling Prediction Sets (RCPS) and Conformal Risk Control (CRC), noting that RCPS offers high‑probability control of population‑mean risk while CRC provides weaker expectation control, and evaluates the effectiveness of the Waudby‑Smith‑Ramdas betting bound versus Hoeffding‑Bentkus bounds for re‑certification of Tier‑1 organs.
arXiv:2609.17545v1 Announce Type: new
Abstract: Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed along...
By Nisreen Albzour, Sarah S. Lam
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
By Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
arXiv:2608. 28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) objectives to address heterogeneous anatomy and acquisition.
By Yang Song, Pengbo Sun, Shichang Feng, Ye Zhu, Xin Xu, Ziran Wang
The paper introduces Cros, a risk‑constrained stopping layer for sequential clinical diagnosis agents that determines when to stop testing and make a diagnosis. Cros combines state‑wise error ranking, policy design on disjoint development splits, and exact tests of selective diagnostic error to provide finite‑sample guarantees. On a MIMIC‑derived abdominal‑pain benchmark, Cros achieves higher state‑error AUROC and lower selective error rates compared to baseline stopping methods, though its performance varies across development resplits.
By Yuexin Wu, Vasile Rus