arXiv:2606. 20115v3 Announce Type: replace Abstract: Conformal risk control (CRC) provides distribution-free segmentation guarantees by calibrating a prediction-set threshold on held-out data.
By Nafis Fuad Shahid
The paper introduces a missingness‑aware conformal calibration method for mortality prediction that accounts for cross‑hospital distribution shifts. By selecting a measurement on an independent sample, grouping patients by whether that measurement is recorded, and applying Mondrian calibration within each group, the method avoids reusing calibration outcomes. Experiments on eICU and MIMIC‑IV data show that, compared to pooled calibration, it reduces the worst‑group coverage gap by a median of 1.9 percentage points across six settings, though the benefit varies with predictor and hospital.
By Liang You, Dongwen Ou, Hengyu Shi, Siyuan Dai
In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, creating a distribution shift that degrades both acc...
arXiv:2609.38112v1 Announce Type: cross
Abstract: Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or misse...
By Bruno Marcondes e Resende, Helton Graziadei, Thiago Rodrigo Ramos, Rafael Izbicki
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
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
By Xin Ci Wong, Duygu Sarikaya, Kieran Zucker, Marc De Kamps, Nishant Ravikumar