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arXiv Computer Vision By Youngung Han, Yului Jeong, Kyeonghun Kim, Dohyun Kweon, Suah Park, Hyunsu Go, Sungha Park, Anna Jung, Jinyong Jun, Yunho Choe, Yunjin Seo, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim

SCINTILLA-SNN: A Spiking Multi-Scale Selective Aggregation Network for Perineural Invasion Prediction

Read the original on arXiv Computer Vision →

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.

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 Computer Vision.

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