arXiv:2607. 11533v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
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.
arXiv:2607. 10988v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.
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.
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:2609.17169v1 Announce Type: cross Abstract: Forecasting anatomical changes such as tumor growth and neurodegeneration is a challenging generative vision task. Morphological evolution is subtle...