# A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI Using Deep Learning

arXiv: https://arxiv.org/abs/2210.14597 · HTML: https://mlsanity.com/paper/2210.14597/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading](https://mlsanity.com/paper/2206.13173/) (2022)
- [SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans](https://mlsanity.com/paper/2205.01683/) (2022)

## Built on by (most cited first, 3 of 3)

- [CrossSpine: Multi-scale Cross-sequence Attention with Anatomical Priors for Automated Pfirrmann Grading](https://mlsanity.com/paper/2607.22728/) (2026)
- [Video-Text Temporal Localization via Multi-Scale Convolution and Dynamic Routing](https://mlsanity.com/paper/2607.05093/) (2026)
- [Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Grading](https://mlsanity.com/paper/2608.04810/) (2026)

## Abstract (arXiv)

This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain. Multiple recent studies investigated different architecture designs, and the most recent success has been attributed to the use of transformer architectures. In this work, we argue that with a well-tuned three-stage pipeline comprising semantic segmentation, localization, and classification, convolutional networks outperform the state-of-the-art approaches. We conducted an ablation study of the existing methods in a population cohort, and report performance generalization across various subgroups. Our code is publicly available to advance research on disc degeneration and low back pain.