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Automated Detection of the Black Hole Sign for Patients with Intracerebral Hemorrhage Using Self-Supervised Learning

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Detection of Brain Haemorrhage Using Convolutional Neural Network

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Oral Cavity Cancer Detection Using Deep Neural Network Models

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Multiclass Intracranial Hemorrhage Detection and Confidence Aware Triage via Deep Ensemble Learning

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Education Research: Validation of an AI-Powered Educational Platform for Neuroanatomical Localization and Stroke Syndrome Recognition

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Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis

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A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection

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Comprehensive Hyperparameter Tuning to Enhance Deep Learning Performance for Intracranial Hemorrhage Classification in Head CT Scans

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MSTBI: Head CT Detection and Prognostic Assessment of Traumatic Brain Injury Dataset

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DAHNet: Dilated Attention Hybrid Network for Intracerebral Hemorrhage Segmentation

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Learning Perspective Distortion Correction in Cone-Beam X-Ray Transmission Imaging

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Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation

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Automated Midline Shift Detection in Head CT Using Localization and Symmetry Techniques Based on User-Selected Slice

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A CAD software application as a decision support system for ischemic stroke detection in the posterior fossa

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An Ensembled Intracranial Hemorrhage (ICH) Subtype Detection and Classification Approach Using A Deep Learning Models

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An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework

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Application of Artificial Intelligence to Welding Mechanics

Shibahara, M., Ikushima, K., & MAEKAWA, M. (2019) · JOURNAL OF THE JAPAN WELDING SOCIETY · doi:10.2207/jjws.88.540

Train, Learn, Expand, RepeatPreprint

Parida, A., Sankar, A., Eisawy, R., Finck, T., Wiestler, B., Pfister, F., & Moosbauer, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08469

Convolutional-LSTM for Multi-Image to Single Output Medical PredictionPreprint

Leal, L. G., Castillo, M., Juarez, F., Ramirez, E., Aspuac, M., & Letona, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10004

Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events

Sharifullin, F. A.-K., Dolotova, D. D., Barmina, T. G., Petrikov, S. S., Сергеевич, К. Л., Ramazanov, G. R., Благосклонова, Е. Р., Архипов, И. В., Skorobogach, I. M., Cheremushkin, N. N., et al. (2020) · Russian Sklifosovsky Journal Emergency Medical Care · doi:10.23934/2223-9022-2020-9-2-231-237

Extraction of cutting plans in craniosynostosis using convolutional neural networks

He, J., Luo, Y., & Gong, J. F. (2020) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei51763.2020.9263523

CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep ModelsPreprint

Quan, Q., Wang, Q., Li, L., Du, Y., & Zhou, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09491

Health data analytics using Internet of things

Singh, V., Udmale, S. S., Pandey, A. K., & Singh, S. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821472-5.00007-7

Putting the Pieces Together: Deep Learning for Knee MRI Multitissue Abnormality Detection and Severity Grading

Li, M., & Chang, C. Y. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210022

Ethical issues on artificial intelligence in radiology: how is it reported in research articles? The current state and future directions

Piotrowski, T., Kaźmierska, J., Mocydlarz-Adamcewicz, M., & Ryczkowski, A. (2021) · Journal of Medical Science · doi:10.20883/medical.e513

Proposta e avaliação de um método de segmentação para Alberta Stroke Program Early CT Score

Brito, R. D. F. (2021) · doi:10.14393/ufu.di.2021.5524

Deep Learning Approaches for Clinical Performance Improvement: Applications to Colonoscopic Diagnosis and Robotic Surgical Skill Assessment

이동헌 (2020) · Seoul National University Open Repository (Seoul National University)

Impact of Artificial Intelligence Integration on Surgical Outcome

Umar, M. (2021) · Journal of the Dow University of Health Sciences · doi:10.36570/jduhs.2021.2.983

New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Care

Dykstra, S., White, J. A., & Gavrilova, M. L. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_17

Segmenting Bones from Brain MRI via Generative Adversarial Learning

Chen, X., Lian, C., Wang, L., Yap, P., Xia, J. J., & Shen, D. G. (2021) · Springer eBooks · doi:10.1007/978-3-030-71881-7_3

Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python

Mohamed, M. E. S., Alamri, A., Smith, B. G., & Uff, C. E. G. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_20

COMMENT ON GREEN'S FUNCTIONS IN TRANSPORT THEORY.

Leuthaeuser, K. D. (1971) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)

Artificial intelligence in thoracic oncology: moving from proof of concept to clinical practice

Ostberg, N. P., & Elefteriades, J. A. (2021) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezab536

Experimental Approach to Monitoring the Degradation Status of Pipelines Transporting Hydrocarbons

Segmentation, A. S. P. E. C. S. R. (2021) · doi:10.33422/4th.msetconf.2021.03.05

BIG DATA DRIVEN CLINICAL INFORMATICS & SURVEILLANCE (BDD_CIS) - A MULTIMODAL DATABASE FOCUSED CLINICAL, COMMUNITY, AND MULTI-OMICS SURVEILLANCE PLAN FOR COVID-19: A STUDY PROTOCOL (Preprint)Preprint

Olatosi, B., Zhang, J., Weissman, S., Li, Z., Hu, J., Hikmet, N., Liang, C., & Li, X. (2020) · doi:10.2196/preprints.24504

INTRACRANIAL HEMORRHAGE DETECTION IN CT SCAN USING DEEP LEARNING

Tharek, A., Muda, A. S., Hudi, A. B., & Huddin, A. B. (2022) · Asian Journal Of Medical Technology · doi:10.32896/ajmedtech.v2n1.1-18 · Cited by 42

Development of a Clinically Applicable Deep Learning System Based on Sparse Training Data to Accurately Detect Acute Intracranial Hemorrhage from Non-enhanced Head Computed Tomography

Wang, H.-C., Wang, S. H., Xiao, F., Ho, U., Lee, C. C., Yan, J., Chen, Y., & Ko, L.-W. (2025) · Neurologia medico-chirurgica · doi:10.2176/jns-nmc.2024-0163 · Cited by 5

Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning

Kuo, W., Häne, C., Mukherjee, P., Malik, J., & Yuh, E. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908021116 · Cited by 281

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Agrawal, D. K., Poonamallee, L., & Joshi, S. (2023) · Indian Journal of Neurotrauma · doi:10.1055/s-0043-1770770 · Cited by 6

Deep learning-assisted detection of intracranial hemorrhage: validation and impact on reader performance

Kang, D.-W., Kim, M., Park, G.-H., Kim, Y., Han, M., Lee, M., Kim, D., Ryu, W., & Jeong, H. (2025) · Neuroradiology · doi:10.1007/s00234-025-03560-x · Cited by 18