A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT Scans
Nguyen, T. N.; Tran, Q. D.; Nguyen, T. N.; Nguyen, Q. H.
Show abstract
We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans. The CNN plays the role of a slice-wise feature extractor while the LSTM is responsible for linking the features across slices. The whole architecture is trained end-to-end with input being an RGB-like image formed by stacking 3 different viewing windows of a single slice. We validate the method on the recent RSNA Intracranial Hemorrhage Detection challenge and on the CQ500 dataset. For the RSNA challenge, our best single model achieves a weighted log loss of 0.0522 on the leaderboard, which is comparable to the top 3% performances, almost all of which make use of ensemble learning. Importantly, our method generalizes very well: the model trained on the RSNA dataset significantly outperforms the 2D model, which does not take into account the relationship between slices, on CQ500. Our codes and models will be made public.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Adaptive Frequency-Spatial Dual-Stream Network (AFS-DSN) for Nasal and Paranasal Sinus CT Segmentation 95%
- Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals 93%
- An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph 93%
Similar papers in this journal
- From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans 95%
- A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) 94%
- Evaluating Large Language Model-Generated Brain MRI Protocols: Performance of GPT4o, o3-mini, DeepSeek-R1 and Qwen2.5-72B 90%
Similar papers in this journal
- Assisting Scalable Diagnosis Automatically via CT Images in the Combat against COVID-19 95%
- Tracking And Predicting COVID-19 Radiological Trajectory Using Deep Learning On Chest X-Rays: Initial Accuracy Testing 94%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 94%
Similar papers in this journal
- ViFIT-assisted Histopathology: From H&E Style Standardization to Virtual Fiber Image Transformation 94%
- Encrypted federated learning for secure decentralized collaboration in cancer image analysis 93%
- Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.