MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis

arXiv:2609.24736 · cs.CV, cs.LG · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: 15 pages, 4 figures, 9 tables. Includes supplementary material

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

Terminology

• Contrastive Learning — 19× in this paper · explained in 5 episodes
• Linear Probing — 17× in this paper · explained in 2 episodes
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• AUC — 11× in this paper · explained in 2 episodes
• Foundation Model — 11× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Embeddings — 9× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Semi-supervised Learning — 4× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Domain Shift — 3× in this paper · explained in 4 episodes
• Knowledge Distillation — 3× in this paper · explained in 11 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Pseudo-labeling — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• World Health Organization — 2× in this paper
• Adaptability — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios
• CAMP: Continuous and Adaptive Learning Model in Pathology
• SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• DINOv2: Learning Robust Visual Features without Supervision

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