SpikeDS: Dual Sparsity Spikformer for Perineural Invasion Prediction in 3D MRI
SpikeDS is a novel spiking neural network architecture that efficiently predicts perineural invasion in cholangiocarcinoma from 3D MRI by leveraging both activation sparsity and spatial sparsity, achieving an AUC of 0.753 with only 14.4 mJ energy consumption on a cohort of 139 patients.
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[Submitted on 13 Jul 2026]
Title:SpikeDS: Dual Sparsity Spikformer for Perineural Invasion Prediction in 3D MRI
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Abstract:Perineural invasion (PNI) is associated with poor prognosis in cholangiocarcinoma (CCA). However, its detection from 3D MRI remains challenging due to the subtle and spatially heterogeneous imaging signatures at the tumor periphery. Capturing such spatially sparse cues necessitates volumetric analysis of 3D MRI, but existing deep learning approaches incur prohibitive computational costs on volumetric medical images, limiting their clinical deployment. We propose Dual Sparsity Spikformer (SpikeDS), a spiking neural network architecture that jointly exploits activation sparsity from binary spike communication and spatial sparsity from window pruning based on firing rates. SpikeDS introduces Dual Sparsity Spiking Attention (DSSA), which combines two complementary mechanisms. The first is Window-based Expert Mixture Spiking Attention (W-EMSA), which selectively applies attention only to salient windows identified by their firing rates. The second is Cross-Window Spiking Self-Attention (CW-SSA), which enables global context exchange through an asymmetric scheme in which pruned windows still contribute as key-value sources. Evaluated on a clinical cohort of 139 CCA patients via 5-fold cross-validation, SpikeDS achieves an AUC of 0.753 while consuming only 14.4 mJ, surpassing the best baseline in both AUC and energy efficiency. These results suggest that dual sparsity provides an effective hardware-aware strategy for improving the efficiency of 3D spiking transformers without compromising diagnostic performance.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.11986 [cs.CV]
(or arXiv:2607.11986v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.11986
arXiv-issued DOI via DataCite
Submission history
From: Dohyun Kweon [view email] [v1] Mon, 13 Jul 2026 14:03:55 UTC (416 KB)
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