Summary of Confidence-based Task Prediction in Continual Disease Classification Using Probability Distribution, by Tanvi Verma and Lukas Schwemer and Mingrui Tan and Fei Gao and Yong Liu and Huazhu Fu
Confidence-Based Task Prediction in Continual Disease Classification Using Probability Distribution
by Tanvi Verma, Lukas Schwemer, Mingrui Tan, Fei Gao, Yong Liu, Huazhu Fu
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper explores the limitations of deep learning models in medical image analysis and proposes a novel approach for continuous learning in dynamic clinical environments. The authors develop a network comprising expert classifiers, which learns to adapt to new tasks by adding new expert classifiers as they arise. They also introduce CTP (Task-Id Predictor), a mechanism that leverages confidence scores and probability distributions (logits) to accurately identify the task being evaluated at inference time. Compared to other continual learning methods, CTP demonstrates superior performance in various benchmarks. By providing CTP with a continuous flow of data during inference, its accuracy can be further improved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how AI models are used for medical image analysis and finds a problem – they don’t adapt well to new situations or changes in the data. The authors suggest a new way to make these models learn continuously by adding more experts as needed. They also create a tool called CTP that can identify which task it’s being asked to perform, even if there are many tasks. This new approach outperforms other similar methods and gets better with more training data. |
Keywords
» Artificial intelligence » Continual learning » Deep learning » Inference » Logits » Probability
