# Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

arXiv: https://arxiv.org/abs/1908.04568 · HTML: https://mlsanity.com/paper/1908.04568/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [A Framework for Interpretability in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2310.01685/) (2023)
- [Explainable AI applications in the Medical Domain: a systematic review](https://mlsanity.com/paper/2308.05411/) (2023)
- [Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification](https://mlsanity.com/paper/2301.00409/) (2023)
- [MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification](https://mlsanity.com/paper/2404.08917/) (2024)
- [A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering](https://mlsanity.com/paper/2306.01750/) (2023)

## Abstract (arXiv)

Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was recently highlighted by ACR Data Science Institute. In this paper we introduce a novel deep learning based approach for the problem of MLS detection, which exploits task-specific structural knowledge. We evaluate our method on a large dataset containing heterogeneous images with significant MLS and show that its mean error approaches the inter-expert variability. Finally, we show the robustness of our approach by validating it on an external dataset, acquired during routine clinical practice.