Summary of Improving Deep Learning Model Calibration For Cardiac Applications Using Deterministic Uncertainty Networks and Uncertainty-aware Training, by Tareen Dawood et al.
Improving Deep Learning Model Calibration for Cardiac Applications using Deterministic Uncertainty Networks and Uncertainty-aware Training
by Tareen Dawood, Bram Ruijsink, Reza Razavi, Andrew P. King, Esther Puyol-Antón
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers focus on improving the calibration performance of deep learning (DL) classification models, which is crucial when using DL in decision-support settings where inaccurate predictions can have negative consequences. They evaluate two types of approaches to enhance DL model calibration: deterministic uncertainty methods (DUMs) and uncertainty-aware training. Specifically, they test three DUMs, two uncertainty-aware training approaches, and their combinations on two realistic clinical applications from cardiac imaging. The results show that both DUMs and uncertainty-aware training can improve accuracy and calibration in both applications, with DUMs generally offering the best improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning (DL) is a type of artificial intelligence used to make predictions or classify things. But what if these predictions aren’t always correct? This paper tries to solve this problem by improving how well DL models are calibrated. Calibration is important because it means that the model can confidently say “I’m not sure” when it’s not really sure about something. In medical imaging, for example, this could help doctors avoid misdiagnosing patients. The researchers tested different methods to improve calibration and found that some worked better than others. They also combined these methods to see if they could work even better together. |
Keywords
» Artificial intelligence » Classification » Deep learning
