arXiv Machine Learning

SpikeDS: Dual Sparsity Spikformer for Perineural Invasion Prediction in 3D MRI

arXiv:2607. 11986v1 Announce Type: cross Abstract: Perineural invasion (PNI) is associated with poor prognosis in cholangiocarcinoma (CCA).

arXiv AI

Jul 14

MMA-Former: Multi-Window Mixture-of-Head Attention Transformer for Adaptive PNI Prediction in 3D MRI

arXiv:2607. 10988v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.

By Youngung Han, Induk Um, Kyeonghun Kim, Junga Kim, Hyunsu Go, Jaewon Jung, Woo Kyoung Jeong, Won Jae Lee, Pa Hong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim

llms

arXiv AI

Jul 14

LoSA-Net: A Localized and Scale-Adaptive Network for Boundary-Sensitive Prediction of Perineural Invasion in 3D MRI

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.

By Youngung Han, Hyunsu Go, Kyeonghun Kim, Induk Um, Junga Kim, Jaewon Jung, Woo Kyoung Jeong, Won Jae Lee, Pa Hong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim

llms safety

arXiv Machine Learning

Jul 14

Adaptive Routing for Efficient Diffusion Transformer-Based PNI Prediction

arXiv:2607. 11533v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.

By Youngung Han, Dohyun Kweon, Kyeonghun Kim, Hyunsu Go, Jina Jeong, Suah Park, Induk Um, Junga Kim, Anna Jung, Yului Jeong, Sungha Park, Jinyong Jun, Pa Hong, Woo Kyoung Jeong, Won Jae Lee, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim

llms diffusion

arXiv Machine Learning

23h ago

Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket

arXiv:2401. 02020v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the challenge of limited performance.

By Zhaokun Zhou, Yijie Lu, Kaiwei Che, Wei Fang, Keyu Tian, Qihao Peng, Yuesheng Zhu, Shuicheng Yan, Yonghong Tian, Li Yuan

llms computer-vision

arXiv Machine Learning

Jun 10

Multimodal Brain Tumour Classification Using Feature Fusion

arXiv:2606. 11107v1 Announce Type: cross Abstract: Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement.

By Wajih ul Islam, Muhammad Yaqoob, Javed Ali Khan, Volker Steuber

multimodal

arXiv AI

Jun 30

BTI-Net: Bidirectional Decoder-Level Task Interaction via Uncertainty-Aware Gating for Multi-Task Medical Image Analysis

arXiv:2606. 29102v1 Announce Type: cross Abstract: Jointly learning to segment and classify medical images demands cross-task synergy, yet encoder-sharing architectures limit decoder reconstruction to task-private representations, permanently discarding the boundary cues and semantic priors each branch could supply to the other.

By Abdullah Al Shafi, Md Kawsar Mahmud Khan Zunayed, Safin Ahmmed, Sk Imran Hossain, Engelbert Mephu Nguifo

computer-vision benchmarks safety

arXiv AI

Jun 24

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.

By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu

computer-vision fine-tuning

arXiv Machine Learning

Jul 28

Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning

arXiv:2602. 05387v2 Announce Type: replace-cross Abstract: MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation.

By Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao, Hsing-Kuo Pao

llms

arXiv AI

Jul 28

Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention

arXiv:2607. 22749v1 Announce Type: cross Abstract: Tracking residual tumor after surgery is essential for catching recurrence early, but automating post-operative glioma segmentation remains a difficult task.

By Alexandru Cri\c{s}an, Diana Borza

llms computer-vision benchmarks

arXiv AI

Aug 7

Ge$^\text{2}$mS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer

arXiv:2604. 08894v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs).

By Zecheng Hao, Shenghao Xie, Kang Chen, Wenxuan Liu, Zhaofei Yu, Tiejun Huang

llms benchmarks

arXiv AI

Jun 19

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.

By Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms rag diffusion nlp benchmarks safety

Hugging Face Trending Papers

Jul 22

Current Injection Spiking Neural Network for Infrared and Visible Image Fusion

Infrared and visible image fusion (IVIF) integrates the complementary information of two modalities into a single image with richer scene content. While existing methods are largely built on artificial neural networks (ANNs), which densely compute over all activations, spiking neural networks (SNNs) communicate through sparse binary spikes and compute only where and when a spike occurs, offering a route to more energy-efficient fusion.

computer-vision multimodal benchmarks

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