Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives
Not on the map. Why?
Abstract (arXiv)
Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information, such as EEG, ECG, EMG and so on. Despite the common data standardisation challenge within medical image analysis domain, the future of deep learning in stroke outcome prediction lie in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 147 references that are in the map. First posted on arXiv 6 Dec 2024.
Builds on
26 of 147 references.
References · 26
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
- 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
- 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 572
- 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
14 more
- 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
- 2017Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentationcited by 58
- 2018Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challengecited by 56
- 2017Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascadecited by 53
- 2024Automated Radiology Report Generation: A Review of Recent Advancescited by 43
- 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
- 2021Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transformcited by 38
- 2016Structured Receptive Fields in CNNscited by 32
- 2018Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learningcited by 12
- 2020Prediction of Thrombectomy Functional Outcomes using Multimodal Datacited by 6
- 2022CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patientscited by 6
- 2018Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome predictioncited by 4
- 2023TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Predictioncited by 3
- 2021Combining unsupervised and supervised learning for predicting the final stroke lesioncited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.