# Improving the Precision of CNNs for Magnetic Resonance Spectral Modeling

arXiv: https://arxiv.org/abs/2409.06609 · HTML: https://mlsanity.com/paper/2409.06609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [DropCluster: A structured dropout for convolutional networks](https://mlsanity.com/paper/2002.02997/) (2020)
- [Accurate, fully-automated NMR spectral profiling for metabolomics](https://mlsanity.com/paper/1409.1456/) (2014)

## Abstract (arXiv)

Magnetic resonance spectroscopic imaging is a widely available imaging modality that can non-invasively provide a metabolic profile of the tissue of interest, yet is challenging to integrate clinically. One major reason is the expensive, expert data processing and analysis that is required. Using machine learning to predict MRS-related quantities offers avenues around this problem, but deep learning models bring their own challenges, especially model trust. Current research trends focus primarily on mean error metrics, but comprehensive precision metrics are also needed, e.g. standard deviations, confidence intervals, etc.. This work highlights why more comprehensive error characterization is important and how to improve the precision of CNNs for spectral modeling, a quantitative task. The results highlight advantages and trade-offs of these techniques that should be considered when addressing such regression tasks with CNNs. Detailed insights into the underlying mechanisms of each technique, and how they interact with other techniques, are discussed in depth.