# Assessing Knee OA Severity with CNN attention-based end-to-end architectures

arXiv: https://arxiv.org/abs/1908.08856 · HTML: https://mlsanity.com/paper/1908.08856/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network](https://mlsanity.com/paper/2106.14292/) (2021)
- [Give me a knee radiograph, I will tell you where the knee joint area is: a deep convolutional neural network adventure](https://mlsanity.com/paper/2202.05382/) (2022)
- [MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography](https://mlsanity.com/paper/2312.02365/) (2023)
- [Better Spanish Emotion Recognition In-the-wild: Bringing Attention to Deep Spectrum Voice Analysis](https://mlsanity.com/paper/2409.05148/) (2024)
- [Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach](https://mlsanity.com/paper/2309.02681/) (2023)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)

## Abstract (arXiv)

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: https://github.com/marc-gorriz/KneeOA-CNNAttention