Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929591436705792 |
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| author | Kaleta, Joanna Skierś, Paweł Dubiński, Jan Korzeniowski, Przemysław Deja, Kamil |
| author_facet | Kaleta, Joanna Skierś, Paweł Dubiński, Jan Korzeniowski, Przemysław Deja, Kamil |
| contents | We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high performance, confidence, and explainability of the models. In this work, we propose to tackle those challenges with a single model that combines standard classification with a diffusion-based generative task in a single shared parametrisation. By sharing representations, our model effectively learns from both labeled and unlabeled data while at the same time providing accurate explanations through counterfactual examples. In our experiments, we show that our Mediffusion achieves results comparable to recent semi-supervised methods while providing more reliable and precise explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation Kaleta, Joanna Skierś, Paweł Dubiński, Jan Korzeniowski, Przemysław Deja, Kamil Computer Vision and Pattern Recognition Machine Learning We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique challenges due to scarce data labelling -- insufficient for standard training, and critical nature of the applications that require high performance, confidence, and explainability of the models. In this work, we propose to tackle those challenges with a single model that combines standard classification with a diffusion-based generative task in a single shared parametrisation. By sharing representations, our model effectively learns from both labeled and unlabeled data while at the same time providing accurate explanations through counterfactual examples. In our experiments, we show that our Mediffusion achieves results comparable to recent semi-supervised methods while providing more reliable and precise explanations. |
| title | Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.09434 |