Papers for
radiology teams
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
MRI order improves prediction of nerve invasion risk in liver cancer
Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma
Abstract: Perineural invasion (PNI) is an adverse histopathologic marker in intrahepatic cholangiocarcinoma (ICC), but it is usually confirmed only after resection. Preoperative T2-weighted MRI may provide noninvasive imaging cues predictive of PNI, although labels are available only at the patient level without slice- or voxel-level annotations. We propose Order-Aware Slab Multiple Instance Learning (OAS-MIL), a weakly supervised framework for patient-level PNI prediction. Each tumor-centered MRI crop is represented as an ordered sequence of overlapping 2.5D slabs formed from contiguous axial slices. A shared encoder extracts slab-level features, which are aggregated by a permutation-invariant set-attention branch and a bidirectional sequence-attention branch. Using five-fold label-stratified cross-validation at the patient level, OAS-MIL achieved a mean AUROC of 0.770, outperforming the evaluated volumetric and MIL baselines. These results suggest that axial order provides a useful inductive bias for weakly supervised PNI prediction from MRI.
Clinician input guides safer medical image segmentation under uncertainty
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Abstract: Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inference. This assumption is fragile when query cases exhibit acquisition shift, atypical pathology, ambiguous boundaries, or poor image quality. Prototype learning, cross-domain matching, interactive segmentation, uncertainty estimation, test-time adaptation, and promptable foundation models address parts of this problem, yet have not been jointly evaluated under a common model of expert attention and clinical risk. This Perspective reframes FSMIS as a sequential clinician-model decision problem with a static support budget $K$ and a distinct interaction budget $B$. At each step, a system accepts the current segmentation, requests feedback, or defers to full expert review. Queries vary in location and modality and are selected by response-conditioned net expected value of information; clinician-provided feedback informs bounded adaptation only after prespecified provenance, consistency, and safety gates. The framework separates distributional atypicality from predicted clinical failure and treats clinician responses as informative but fallible observations. We synthesize the transition from few-shot and cross-domain segmentation to interactive and selective adaptation, delineate the integration gap, and define four research directions with falsifiable hypotheses. Evaluation spans external-domain calibration, quality-effort trade-offs, reader studies, and prospective workflow assessment. The central claim is not that interaction alone resolves domain shift, but that scarce expert attention should be allocated only when it is expected to reduce clinically relevant risk.