MTMed3D: A Multi-Task Transformer-Based Model for 3D Medical Imaging

arXiv:2511.12373 · eess.IV, cs.CV · Submitted 2025-11-15 · Read on arXiv

eess.IV, cs.CV

Submitted: 2025-11-15

Updated: 2026-09-18

Code: https://github.com/fanlimua/MTMed3D

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

Terminology

• Transformer — 67× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Multi-objective Optimization — 2× in this paper · explained in 4 episodes
• Two-Stage Framework — 2× in this paper · explained in 2 episodes
• U-net architecture — 2× in this paper · explained in 3 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• GradNorm with the Multiple Gradient Descent Algorithm — 1× in this paper
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Low-Grade Glioma — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training
• Neural Machine Translation by Jointly Learning to Align and Translate
• Long Short-Term Memory-Networks for Machine Reading
• Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering

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