# Prototype Knowledge Distillation for Medical Segmentation with Missing Modality arXiv: https://arxiv.org/abs/2303.09830 ยท HTML: https://mlsanity.com/paper/2303.09830/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. - Builds on 14 of its 18 references that are in the map. - First posted on arXiv 17 Mar 2023. ## Placed in - Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/) ## Builds on (most cited first, 14 of 14) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017) - [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016) - [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014) - [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015) - [mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation](https://mlsanity.com/paper/2206.02425/) (2022) - [Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation](https://mlsanity.com/paper/1907.11150/) (2019) - [Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion](https://mlsanity.com/paper/2002.09708/) (2020) - [Knowledge distillation from multi-modal to mono-modal segmentation networks](https://mlsanity.com/paper/2106.09564/) (2021) - [GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges](https://mlsanity.com/paper/2202.06511/) (2022) - [Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2104.06231/) (2021) - [CollaGAN : Collaborative GAN for Missing Image Data Imputation](https://mlsanity.com/paper/1901.09764/) (2019) - [Multi-Domain Image Completion for Random Missing Input Data](https://mlsanity.com/paper/2007.05534/) (2020) - [Rethinking Alignment and Uniformity in Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2211.14513/) (2022) ## Built on by (most cited first, 24 of 24) - [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024) - [Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models](https://mlsanity.com/paper/2404.04936/) (2024) - [Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality](https://mlsanity.com/paper/2401.13898/) (2024) - [PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates](https://mlsanity.com/paper/2407.14796/) (2024) - [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024) - [Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity](https://mlsanity.com/paper/2403.09428/) (2024) - [MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment](https://mlsanity.com/paper/2408.09465/) (2024) - [Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation](https://mlsanity.com/paper/2505.24361/) (2025) - [Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation](https://mlsanity.com/paper/2411.06106/) (2024) - [Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation](https://mlsanity.com/paper/2305.07881/) (2023) - [Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation](https://mlsanity.com/paper/2406.12254/) (2024) - [Adaptive Conformal Guidance for Learning under Uncertainty](https://mlsanity.com/paper/2502.16736/) (2025) - [A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models](https://mlsanity.com/paper/2308.01328/) (2023) - [A Multimodal Feature Distillation with Mamba-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities](https://mlsanity.com/paper/2404.14019/) (2024) - [Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data](https://mlsanity.com/paper/2311.18758/) (2023) - [Complementary Information Mutual Learning for Multimodality Medical Image Segmentation](https://mlsanity.com/paper/2401.02717/) (2024) - [Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning](https://mlsanity.com/paper/2406.08634/) (2024) - [RMMSS: Towards Advanced Robust Multi-Modal Semantic Segmentation with Hybrid Prototype Distillation and Feature Selection](https://mlsanity.com/paper/2505.12861/) (2025) - [From Swath to Full-Disc: Advancing Precipitation Retrieval with Multimodal Knowledge Expansion](https://mlsanity.com/paper/2506.07050/) (2025) - [DMAF-Net: An Effective Modality Rebalancing Framework for Incomplete Multi-Modal Medical Image Segmentation](https://mlsanity.com/paper/2506.11691/) (2025) - [Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using H"older Divergence and Mutual Information-Enhanced Knowledge Transfer](https://mlsanity.com/paper/2507.01254/) (2025) - [Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging](https://mlsanity.com/paper/2604.00985/) (2026) - [Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging](https://mlsanity.com/paper/2605.14654/) (2026) - [VeCAS: Vessel-Focused Contrast-Free Angiogram Synthesis for Vascular Interventions](https://mlsanity.com/paper/2608.22828/) (2026) ## Abstract (arXiv) Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in clinical is difficult due to the limitation of the scan time and other clinical situations. As such, it is clinically meaningful to develop an image segmentation paradigm to handle this missing modality problem. In this paper, we propose a prototype knowledge distillation (ProtoKD) method to tackle the challenging problem, especially for the toughest scenario when only single modal data can be accessed. Specifically, our ProtoKD can not only distillate the pixel-wise knowledge of multi-modality data to single-modality data but also transfer intra-class and inter-class feature variations, such that the student model could learn more robust feature representation from the teacher model and inference with only one single modality data. Our method achieves state-of-the-art performance on BraTS benchmark. The code is available at urlhttps://github.com/SakurajimaMaiii/ProtoKD.