# Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

arXiv: https://arxiv.org/abs/2208.03403 · HTML: https://mlsanity.com/paper/2208.03403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 5 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [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)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels](https://mlsanity.com/paper/2005.12734/) (2020)
- [Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans](https://mlsanity.com/paper/1803.05854/) (2018)
- [A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans](https://mlsanity.com/paper/1902.03233/) (2019)
- [VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs](https://mlsanity.com/paper/2106.12930/) (2021)
- [A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms](https://mlsanity.com/paper/2112.04490/) (2021)
- [DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans](https://mlsanity.com/paper/2108.06490/) (2021)
- [Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling](https://mlsanity.com/paper/2203.11206/) (2022)
- [Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.06486/) (2021)
- [A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographs](https://mlsanity.com/paper/2104.02256/) (2021)

## Built on by (most cited first, 1 of 1)

- [CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking](https://mlsanity.com/paper/2601.02521/) (2026)

## Abstract (arXiv)

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train slice-level classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.