arXiv:2607. 10992v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a clinically relevant indicator of tumor aggressiveness and can influence surgical decision-making, motivating interest in reliable preoperative assessment.
arXiv:2607. 10988v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.
arXiv:2607. 11533v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.
SCINTILLA‑SNN is a 3D spiking neural network designed to predict perineural invasion (PNI) in cholangiocarcinoma (CCA) from MRI scans. It uses a four‑stage hierarchical backbone and a Multi‑Scale Spike Aggregation module to focus on sparse, localized PNI cues while reducing computational load. In a 10‑year retrospective study of 182 patients, the model achieved an AUROC of 0.748 and cut inference energy by over 23× compared to dense MAC‑only approaches.
The paper introduces Order-Aware Slab Multiple Instance Learning (OAS‑MIL) for predicting perineural invasion (PNI) in intrahepatic cholangiocarcinoma using preoperative T2‑weighted MRI. It treats each tumor‑centered MRI crop as an ordered sequence of overlapping 2.5D slabs, extracting slab‑level features with a shared encoder and aggregating them through both permutation‑invariant set‑attention and bidirectional sequence‑attention branches. In five‑fold patient‑level cross‑validation, OAS‑MIL achieved a mean AUROC of 0.770, outperforming volumetric and MIL baselines, indicating that axial order provides a useful inductive bias for weakly supervised PNI prediction.
arXiv:2605.05522v3 Announce Type: replace-cross Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...
arXiv:2610.00279v1 Announce Type: new Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...
The paper introduces MSM‑Seg, a dual‑memory segmentation framework for 3D multi‑modal brain tumor segmentation. It combines a modality‑and‑slice memory attention module to capture cross‑modal and spatial‑slice dependencies, a multi‑scale category‑agnostic prompt encoder for whole‑tumor guidance, and a modality‑adaptive fusion decoder to integrate complementary decoding information. Experiments on various MRI datasets show that MSM‑Seg surpasses state‑of‑the‑art methods for metastases and glioma tumor segmentation.
NeuroTS-Net is a 3‑D encoder‑decoder CNN designed for multi‑class semantic segmentation of pediatric brain tumors in multi‑modal MRI. It uses a dual‑scale raw‑detail stream, adaptive low‑resolution context selection, and detail‑preserving multipath downsampling to maintain fine intensity and boundary information while modeling broader tumor context. Trained on the BraTS 2026 pediatric dataset, it outperformed nnU‑Net and MedNeXt, achieving Dice scores of 0.938/0.937 on internal validation and 0.927/0.926 on the official challenge set.
The paper introduces Order‑Aware Slab Multiple Instance Learning (OAS‑MIL), a weakly supervised method that predicts perineural invasion (PNI) risk in intrahepatic cholangiocarcinoma using preoperative T2‑weighted MRI. Tumor‑centered MRI crops are split into ordered 2.5D slabs, and a shared encoder extracts features that are aggregated through both permutation‑invariant set‑attention and bidirectional sequence‑attention branches. In five‑fold patient‑level cross‑validation, OAS‑MIL achieved a mean AUROC of 0.770, outperforming volumetric and other MIL baselines, indicating that preserving axial order improves non‑invasive PNI prediction.
arXiv:2607. 11986v1 Announce Type: cross Abstract: Perineural invasion (PNI) is associated with poor prognosis in cholangiocarcinoma (CCA).
arXiv:2607. 19137v1 Announce Type: cross Abstract: Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy.
Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy. Optimizing only paired pixel fidelity can suppress uncertain lesion enhancement, whereas adversarial or stochastic generative objectives can favor realistic post-contrast appearance without guaranteeing patient-specific lesion fidelity.