# Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability

arXiv: https://arxiv.org/abs/2108.08136 · HTML: https://mlsanity.com/paper/2108.08136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [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)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations](https://mlsanity.com/paper/1912.09024/) (2019)
- [Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging](https://mlsanity.com/paper/2008.02766/) (2020)
- [Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention](https://mlsanity.com/paper/1907.04052/) (2019)
- [Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset](https://mlsanity.com/paper/2103.08590/) (2021)
- [Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet)](https://mlsanity.com/paper/2005.02706/) (2020)
- [Deep Learning for Musculoskeletal Image Analysis](https://mlsanity.com/paper/2003.00541/) (2020)
- [A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset](https://mlsanity.com/paper/2010.01947/) (2020)

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

- [Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model](https://mlsanity.com/paper/2411.12783/) (2024)
- [Identifying Spurious Correlations and Correcting them with an Explanation-based Learning](https://mlsanity.com/paper/2211.08285/) (2022)
- [Distance-Aware eXplanation Based Learning](https://mlsanity.com/paper/2309.05548/) (2023)

## Abstract (arXiv)

This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis. As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane). For multi-plane, we investigate various methods of fusing the planes in the network. This analysis resulted in the novel 'MPFuseNet' network and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977 and 0.957 respectively. We then developed an objective metric, Penalised Localisation Accuracy (PLA), to validate the model's localisation ability. This metric compares binary masks generated from Grad-Cam output and the radiologist's annotations on a sample of MRIs. We also extracted explainability features in a model-agnostic approach that were then verified as clinically relevant by the radiologist.