FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation

arXiv:2609.24627 · cs.CV, cs.AI · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: This manuscript has been accepted for publication at the 7th International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2026)

Code: https://github.com/AshknMrd/FedMust

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Federated Learning — 25× in this paper · explained in 14 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Federated Learning (FL) — 4× in this paper · explained in 7 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Distillation — 3× in this paper · explained in 2 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation
• The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

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