Summary of Nyctale: Neuro-evidence Transformer For Adaptive and Personalized Lung Nodule Invasiveness Prediction, by Sadaf Khademi et al.
NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction
by Sadaf Khademi, Anastasia Oikonomou, Konstantinos N. Plataniotis, Arash Mohammadi
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Image and Video Processing (eess.IV)
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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 introduces the NYCTALE framework, a neuro-inspired Transformer architecture inspired by evidence accumulation in primate brains. By combining models from cognitive psychology and neuroscience, the authors propose a novel approach for personalized lung cancer diagnosis in Personalized Medicine (PM). Unlike conventional CT-based Deep Learning (DL) models, NYCTALE processes data dynamically and adaptively, making predictions only when sufficient evidence is accumulated. In preliminary experiments using an in-house dataset of 114 subjects, NYCTALE outperforms benchmark accuracy even with 60% less training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help doctors diagnose lung cancer more accurately by using ideas from how our brains work. It’s like when you’re trying to find something in the dark – you need to gather clues before you can make a decision. The NYCTALE framework is like that, but for CT scans of lungs. Instead of looking at all the scan slices at once, it looks at them one by one and makes a prediction only when it has enough information. The results are promising, showing that this new approach works well even with less training data. |
Keywords
* Artificial intelligence * Deep learning * Transformer
