ArticleSensors (Basel, Switzerland)2020
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 1 citations in OpenAlex.
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- A randomized multi-window 3D deep learning approach for intracranial hemorrhage detection on non-contrast head CT.Medical & biological engineering & computing · 2026Article
- TrustNet: a lightweight network with integrated uncertainty quantification and quantitative explainable AI for ischemic stroke detection in CT images.Scientific reports · 2026Article
- Article
- Robust slice-level stroke classification in non-contrast head CT via structural consistency regularization and counterfactual suppression.Frontiers in bioengineering and biotechnology · 2026Article
- Deep learning for intracranial hemorrhage detection and classification in brain CT scans: a systematic review and hybrid model approach.Frontiers in digital health · 2026Review
- Deep Learning Enhances Weightbearing CT Detection of Lisfranc Instability: A FIXUS-AI Ankle Insight 3D Algorithm.Cureus · 2025Article
- Intracranial hemorrhage segmentation and classification framework in computer tomography images using deep learning techniques.Scientific reports · 2025Article
- Automated Hematoma Detection and Outcome Prediction in Patients With Traumatic Brain Injury.CNS neuroscience & therapeutics · 2024Article
- Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.Frontiers in neuroscience · 2024Article
- PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.BMC cancer · 2023Article
- Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- A fast and accurate brain extraction method for CT head images.BMC medical imaging · 2023Article
- Development of a Deep Learning Model for Retinal Hemorrhage Detection on Head Computed Tomography in Young Children.JAMA network open · 2023Article
- An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks.Brain sciences · 2023Article
- Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning Applied to Intracranial Hemorrhage Detection.Journal of imaging · 2023Article
- Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm.Frontiers in neuroscience · 2023Article
- Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles.Frontiers in neurology · 2023Article
- Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.