Perineural Invasion Prediction

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9 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma
Sep 10, 2026Hyunsu Go, Youngung Han, Kyeonghun Kim +11Perineural Invasion Prediction Weakly Supervised Learning
Seoul National University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea · Chung-Ang University, Seoul, Republic of Korea +3
SCINTILLA-SNN: A Spiking Multi-Scale Selective Aggregation Network for Perineural Invasion Prediction
Sep 10, 2026Youngung Han, Yului Jeong, Kyeonghun Kim +11Perineural Invasion Prediction Multi-Scale Feature Fusion
Seoul National University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea · Kyung Hee University, Seoul, Republic of Korea +2
Anatomy-Privileged Distillation with Token Routing for MRI-Based Prediction of Perineural Invasion
Jul 13, 2026Hyunsu Go, Youngung Han, Kyeonghun Kim +15Perineural Invasion Prediction Medical Image Classification
Seoul National University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea · Kyung Hee University, Seoul, Republic of Korea +5
SpikeDS: Dual Sparsity Spikformer for Perineural Invasion Prediction in 3D MRI
Jul 13, 2026Induk Um, Youngung Han, Kyeonghun Kim +14Spiking Transformers 3D Medical Imaging
Chung-Ang University, Seoul, Republic of Korea · Seoul National University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea +5
Adaptive Routing for Efficient Diffusion Transformer-Based PNI Prediction
Jul 13, 2026Youngung Han, Dohyun Kweon, Kyeonghun Kim +15Diffusion Transformer Perineural Invasion Prediction
Seoul National University, Seoul, Republic of Korea · Kyung Hee University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea +5
LoSA-Net: A Localized and Scale-Adaptive Network for Boundary-Sensitive Prediction of Perineural Invasion in 3D MRI
Jul 13, 2026Youngung Han, Hyunsu Go, Kyeonghun Kim +9Self-Attention Perineural Invasion Prediction
Seoul National University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea · Chung-Ang University, Seoul, Republic of Korea +3
MMA-Former: Multi-Window Mixture-of-Head Attention Transformer for Adaptive PNI Prediction in 3D MRI
Jul 13, 2026Youngung Han, Induk Um, Kyeonghun Kim +93D ViTs 3D Medical Imaging
Seoul National University, Seoul, Republic of Korea · Chung-Ang University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea +3
NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification
Mar 31, 2026Youngung Han, Minkyung Cha, Kyeonghun Kim +12Perineural Invasion Prediction Clinical Prediction
Seoul National University, South Korea · OUTTA, South Korea · Chung-Ang University, South Korea +5
Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer
Mar 6, 2026Sarah S. L. Chow, Rui Wang, Robert B. Serafin +10Oncology Computational Pathology
Department of Pathology, Stanford University, Stanford, CA 94305 USA · University of Washington, Seattle, WA 98195 USA · University of Chicago, Chicago, IL 60637 USA +6

Sep 10, 2026J/K move · Enter open · S save

Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma

Hyunsu Go, Youngung Han, Kyeonghun Kim, Jinyong Jun+10

Seoul National University, Seoul, Republic of Korea · OUTTA, Seoul, Republic of Korea · Chung-Ang University, Seoul, Republic of Korea+3

Perineural Invasion Prediction Weakly Supervised Learning Clinical Prediction Multiple Instance Learning

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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.