Summary of Mediffusion: Joint Diffusion For Self-explainable Semi-supervised Classification and Medical Image Generation, by Joanna Kaleta et al.

Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation

by Joanna Kaleta, Paweł Skierś, Jan Dubiński, Przemysław Korzeniowski, Kamil Deja

First submitted to arxiv on: 12 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary Mediffusion is a novel method for semi-supervised learning with explainable classification, developed to address the unique challenges of medical imaging. The approach combines standard classification with a diffusion-based generative task in a single shared parameterization, allowing it to learn from both labeled and unlabeled data while providing accurate explanations through counterfactual examples. In experiments, Mediffusion achieves results comparable to recent semi-supervised methods while offering more reliable and precise explanations.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Mediffusion is a new way to teach machines to make smart decisions without needing lots of labeled data. Medical imaging has special challenges because there isn’t enough labeled data, and the applications are super important. Mediffusion solves this problem by combining two tasks – normal classification and generating images – into one model. This lets it learn from both labeled and unlabeled data while giving explanations for its decisions. Tests show that Mediffusion works as well as other methods but provides better explanations.