works on

From the 1 of 5 linked papers with an AI index.

most citedThe TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

12 citations · 12 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV202612 cited

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

Kaiyuan Yang, Fabio Musio, Yihui Ma +112

The paper introduces the TopCoW Challenge, a benchmark for automatically segmenting the Circle of Willis in CT and MR angiography using deep learning, and provides a new annotated…

cs.LG2026

SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression

Zehang Lin, Miao Yang, Haihan Zhu +9

The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by…

cs.NI2026

NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning

Zhen Fang, Miao Yang, Zehang Lin +6

The expanding scale of neural networks poses a major challenge for distributed machine learning, particularly under limited communication resources. While split learning (SL) allev…

cs.LG2026

Conflict-Aware Client Selection for Multi-Server Federated Learning

Mingwei Hong, Zheng Lin, Zehang Lin +7

Federated learning (FL) has emerged as a promising distributed machine learning (ML) that enables collaborative model training across clients without exposing raw data, thereby pre…

cs.LG2025

SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression

Zehang Lin, Zheng Lin, Miao Yang +7

The increasing complexity of neural networks poses a significant barrier to the deployment of distributed machine learning (ML) on resource-constrained devices, such as federated l…