Summary of Informed Deep Abstaining Classifier: Investigating Noise-robust Training For Diagnostic Decision Support Systems, by Helen Schneider et al.
Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems
by Helen Schneider, Sebastian Nowak, Aditya Parikh, Yannik C. Layer, Maike Theis, Wolfgang Block, Alois M. Sprinkart, Ulrike Attenberger, Rafet Sifa
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a deep learning approach to optimizing clinical workflows by developing image-based diagnostic decision support systems (DDSS). The proposed system leverages natural language processing to annotate image data using radiological databases, reducing the need for manual annotation. To address label noise in “real-world” datasets, the authors introduce an informed deep abstaining classifier (IDAC) loss function that incorporates noise level estimations during training. Compared to existing noise-robust losses and a baseline DAC loss, IDAC demonstrates enhanced noise robustness on both simulated and real-world chest X-ray datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to help doctors make better decisions by analyzing medical images. Right now, making these systems is time-consuming and expensive because someone has to manually label each image. This paper shows how to use natural language processing to automatically label images, which could make a big difference in saving time and money. The researchers also came up with a new way to train their system that makes it more robust to mistakes or “noise” in the data. They tested this approach on both fake and real chest X-ray images and found that it worked better than existing methods. |
Keywords
» Artificial intelligence » Deep learning » Loss function » Natural language processing
