[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86398-en":3,"doc-seo-86398-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86398,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","SegWithU Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation","Reliable uncertainty estimation is critical for medical image segmentation because automated contours drive downstream quantification and clinical decision support. Many top uncertainty approaches require repeated inference, while efficient single-forward-pass methods often produce weaker failure ranking or assume restrictive feature-space geometry. SegWithU introduces a post hoc framework that augments a frozen pretrained backbone with a lightweight uncertainty head. It outputs two voxel-wise uncertainty maps for calibration via probability tempering and for ranking-based error detection/selective prediction. Across ACDC, BraTS2024, and LiTS, it yields strong AUROC/AURC while preserving segmentation quality, supporting practical reliability-aware deployment.","SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation  \narXiv :2604 . 15271v3 [ cs .CV] 10 Jul 2026  \n Tianhao Fu 1, 4, 5, 6, *  Austin Wang †, 2, 5, 6  Charles Chen †, 1, 5, 6  \n Roby Aldave-Garza †, 3, 5, 6  Yucheng Chen 5, 7  \n† Equal contributions.  \n1 University of Toronto, Toronto, ON, Canada  \n2 McGill University, Montreal, QC, Canada  \n3 University of Waterloo, Waterloo, ON, Canada  \n4 Vector Institute, Toronto, ON, Canada  \n5 Project Neura, Toronto, ON, Canada  \n6 University of Toronto Machine Intelligence Student Team, Toronto, ON, Canada  \n7 Amplimit, Toronto, ON, Canada  \nAbstract  \nReliable uncertainty estimation is critical for medi cal image segmentation, where automated contours feed downstream quantification and clinical decision support. Many strong uncertainty methods require repeated inference, while efficient single-forward-pass alternatives often provide weaker failure ranking or rely on restrictive feature-space assumptions. We present SegWithU, a posthoc framework that augments a frozen pretrained segmentation backbone with a lightweight uncertainty head. SegWithU taps intermediate backbone features and models uncertainty as perturbation energy in a compact probe space using rank-1 posterior probes. It produces two voxel-wise uncertainty maps: a calibration-oriented map for probability tempering and a ranking-oriented map for error detection and selective prediction. Across ACDC, BraTS2024, and LiTS, SegWithU is the strongest and most consistent single-forward-pass baseline, achieving AUROC/AURC of 0.9838/2 .4885, 0.9946/0 .2660, and 0.9925/0 .8193, respectively, while preserving segmentation quality. These results suggest that perturbation-based uncertainty modeling is an effective and practical route to reliability-aware medical segmentation.  \nSource code is available at [https://github.com/](https://github.com/)[ ](https://github.com/)[ProjectNeura/SegWithU](ProjectNeura/SegWithU.)[.](ProjectNeura/SegWithU.)  \n* Project lead with correspondence at [terry.fu@projectneura.org](terry.fu@projectneura.org).  \n1. Introduction  \nMedical image segmentation is now a core tool in computational medicine, underpinning anatomical quantification, lesion burden estimation, treatment planning, and longitudinal disease assessment. The success of modern segmentation systems, exemplified by highly optimized frameworks such as nnU-Net, has made accurate voxel-wise delineation increasingly accessible across organs and imaging modalities. Yet in clinical use, segmentation is rarely an end in itself. It is a quantitative instrument whose errors propagate into downstream measurements and decisions. A contour that looks plausible is therefore not necessarily one that should be trusted. Reliable deployment requires not only accurate segmentation, but also an explicit indication of when and where the prediction may be unreliable. [9]  \nUncertainty estimation offers a natural mechanism for this kind of reliability awareness. In medical image analysis, uncertainty maps can highlight ambiguous tissue interfaces, regions degraded by noise or motion, atypical pathology, and cases that warrant expert review. Prior work in Bayesian deep learning distinguishes epistemic uncertainty, which reflects model uncertainty, from aleatoric uncertainty, which reflects irreducible data uncertainty; both matter in dense prediction problems such as segmentation.  \n[10] In principle, such signals can support quality control, selective automation, failure triage, and uncertainty-aware downstream measurements. In practice, however, obtaining uncertainty estimates that are both useful and deployable remains difficult.  \nMany existing uncertainty methods impose substantial training or inference overhead. Deep ensembles often provide strong uncertainty quality, but require training and storing multiple models. [11] Monte Carlo dropout approximates Bayesian inference through repeated stochastic forwar","cbCainSfJQ402uSf","https://ap.wps.com/l/cbCainSfJQ402uSf","pdf",9371289,1,31,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is uncertainty estimation important in medical image segmentation?\",\"answer\":\"Segmentation errors propagate into downstream measurements and clinical decisions. Uncertainty maps help indicate when and where predictions may be unreliable, supporting quality control and expert review.\"},{\"question\":\"What problem does SegWithU address compared with existing uncertainty methods?\",\"answer\":\"Many strong methods need repeated inference or ensembles, and some efficient single-pass approaches rely on restrictive feature-space assumptions. SegWithU targets deployable post hoc uncertainty without retraining the segmentation backbone.\"},{\"question\":\"How does SegWithU generate uncertainty in a single forward pass?\",\"answer\":\"SegWithU taps intermediate backbone features and models uncertainty as perturbation energy in a compact probe space using rank-1 posterior probes. It produces voxel-wise maps for calibration-oriented tempering and ranking-oriented error detection/selective prediction.\"}]",1784211494,78,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"segwithu-uncertainty-as-perturbation-energy-for-single-forward-pass-risk-aware-medical-image-segmentation","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/segwithu-uncertainty-as-perturbation-energy-for-single-forward-pass-risk-aware-medical-image-segmentation/86398/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is uncertainty estimation important in medical image segmentation?","Question",{"text":74,"@type":75},"Segmentation errors propagate into downstream measurements and clinical decisions. Uncertainty maps help indicate when and where predictions may be unreliable, supporting quality control and expert review.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What problem does SegWithU address compared with existing uncertainty methods?",{"text":79,"@type":75},"Many strong methods need repeated inference or ensembles, and some efficient single-pass approaches rely on restrictive feature-space assumptions. SegWithU targets deployable post hoc uncertainty without retraining the segmentation backbone.",{"name":81,"@type":72,"acceptedAnswer":82},"How does SegWithU generate uncertainty in a single forward pass?",{"text":83,"@type":75},"SegWithU taps intermediate backbone features and models uncertainty as perturbation energy in a compact probe space using rank-1 posterior probes. 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