The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer
Santaolalla, A., Hulsen, T., Davis, J., Ahmed, H.U., Moore, C.M., Punwani, S., Attard, G., McCartan, N., Emberton, M., Coolen, A., Van
Hemelrijck, M. (2021). Frontiers in Artificial Intelligence, 4:162.
Literature analysis of artificial intelligence in biomedicine
Hulsen, T. (2021). Pharmacogenomics Res Pers Med, pre-print.
Challenges and Solutions for Big Data in Personalized Healthcare
Hulsen, T. (2021). In: Big Data in Psychiatry and Neurology (pp69-94). Elsevier.
Performance Requirements for Cough Classifiers in real-world applications
Brinker, A.C. den, Coman, M., Ouweltjes, O., Crooks, M.G., Thackray-Nocera, S., Morice, A.H. (2021). Proceedings of the 28th European Signal Processing Conference, EUSIPCO 2020, Jan. 2021, Amsterdam, The Netherlands, 96-100.
Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance
Gilst, M.M. van, Wulterkens, B., Fonseca, P., Radha, M., Ross, M., … Overeem, S. (2020). BMC Research Notes, 13(513).
Guidewire segmentation in 4D ultrasound using recurrent fully convolutional networks
Lee, B.C., Vaidya, K., Jain, A.K., Chen, A. (2020). Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. PIPPI 2020, Lecture Notes in Computer Science book series, volume 12437, Lima, Peru, October 4-8, 2020, 55-65.
Persuasion-Induced Physiology Predicts Persuasion Effectiveness
Spelt, H.A.A., Zhang, C., Westerink, J.H.D.M., Ham, J., IJsselsteijn, W. (2020). IEEE Transactions on Affective Computing.
Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm
Baloescu, C., Toporek, G., Kim, S., McNamara, K., Liu., R., Shaw, M.M., … Moore, C.L. (2020). IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(11), 2312-2320.
Continuous-Spectrum Infrared Illuminator for Camera-PPG in Darkness
Wang, W., Vosters, L., Brinker, A.C. den (2020). Sensors, 20(11), 3044.
Sharing Is Caring – Data Sharing Initiatives in Healthcare
Hulsen, T. (2020). International Journal of Environmental Research and Public Health, 17(9), 3046.
Visual Analytics for Hypothesis-Driven Exploration in Computational Pathology
Corvo, A., Garcia Caballero, H.S., Westenberg, M.A., Driel, M.A. van, Wijk, J.J. van (2020). IEEE Transactions on Visualization and Computer Graphics.
Learning Metal Artifact Reduction in Cardiac CT images with moving pacemakers
Lossau, T. (née Elss), Nickisch, H., Wissel T., Morlock M., Grass, M. (2020). Medical Image Analysis, 61, 101655.
Data Augmentation and Semi-supervised Learning for Deep Neural Networks-based Text Classifier
Shim, H.,Luca, S., Lowet, D., Vanrumste, B., Stijn L. (2020). Proceedings of the 35th Annual ACM Symposium on Applied Computing, SAC '20, March 2020, pp. 1119–1126.
Combining deep learning and model-based segmentation for labeled spine CT segmentation
Buerger, C., Berg, J. von, Franz, A., Klinder, T., Lorenz, C., Lenga, M. (2020). Proceedings SPIE, Volume 11313, Medical Imaging 2020: Image Processing; 113131C.
Correction of motion artifacts using a multiscale fully convolutional neural network
Sommer, K., Saalbach, A., Brosch, T., Hall, C., Cross, N.M., Andre, J.B. (2020). American Journal of Neuroradiology, 41(3), 416-423.
Modified RGB Cameras for Infrared Remote-PPG
Wang, W., Brinker, A.C. den (2020). IEEE Transactions on Biomedical Engineering, 67, 2893 – 2904.
Automatically Localizing a Large Set of Spatially Correlated Key Points: A Case Study in Spine Imaging
Mader, A. O., Lorenz, C., von Berg, J., & Meyer, C. (2019, October). In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 384-392). Springer, Cham.
How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning
Blendowski, M., Nickisch, H., & Heinrich, M. P. (2019, October). In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 649-657). Springer, Cham.
Recurrent Sub-volume Analysis of Head CT Scans for the Detection of Intracranial Hemorrhage
Vidya, M. S., Mallya, Y., Shastry, A., & Vijayananda, J. (2019, October). In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 864-872). Springer, Cham.
Unpaired Synthetic Image Generation in Radiology Using GANs
Prokopenko, D., Stadelmann, J. V., Schulz, H., Renisch, S., & Dylov, D. V. (2019, October). In Workshop on Artificial Intelligence in Radiation Therapy (pp. 94-101). Springer, Cham.
Deep Text Prior: Weakly Supervised Learning for Assertion Classification
Liventsev, V., Fedulova, I., & Dylov, D. (2019, September). In International Conference on Artificial Neural Networks (pp. 243-257). Springer, Cham.
Pseudo-CT image generation from mDixon MRI images using fully convolutional neural networks
Stadelmann, J. V., Schulz, H., van der Heide, U. A., & Renisch, S. (2019, March). In Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10953, p. 109530Z). International Society for Optics and
Photonics.
Cardiac arrhythmia detection using deep learning: A review
Parvaneh, S., Rubin, J., Babaeizadeh, S., & Xu-Wilson, M. (2019). Journal of electrocardiology.
Pneumothorax Detection and Localization in Chest Radiographs: A Comparison of Deep Learning Approaches
Gooßen, A., Deshpande, H., Harder, T., Schwab, E., Baltruschat, I., Mabotuwana, T., ... & Saalbach, A. (2019).
Automatic delineation of anterior and posterior cruciate ligaments by combining deep learning and deformable atlas based segmentation
Mallya, Y., Vijayananda, J., Vidya, M. S., Venugopal, V. K., & Mahajan, V. (2019, March). In Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10953, p. 1095321). International Society for Optics and Photonics.
Local and global transformations to improve learning of medical images applied to chest radiographs
Vidya, M. S., Krishnan, M., Anirudh, G., Kundeti, S. R., & Vijayananda, J. (2019, March). In Medical Imaging 2019: Image Processing (Vol. 10949, p. 1094936). International Society for Optics and Photonics.
Offset regression networks for view plane estimation in 3D fetal ultrasound
Schmidt-Richberg, A., Schadewaldt, N., Klinder, T., Lenga, M., Trahms, R., Canfield, E., ... & Lorenz, C. (2019, March). In Medical Imaging 2019: Image Processing (Vol. 10949, p. 109493K). International Society for Optics and Photonics.
Neuronale Netze zur Pathologiedetektion bei Röntgenthoraxuntersuchungen: Verbesserung durch intelligente Vorverarbeitung
Baltruschat, I., Grass, M., Saalbach, A., Nickisch, H., von Berg, J., Steinmeister, L., ... & Adam, G. (2019, March). In RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (Vol. 191, No. S 01, pp. WISS-205). Georg Thieme Verlag KG.
Prospective acceleration of parallel RF transmission‐based 3D chemical exchange saturation transfer imaging with compressed sensing
Heo, H. Y., Xu, X., Jiang, S., Zhao, Y., Keupp, J., Redmond, K. J., ... & Zhou, J. (2019). Magnetic resonance in medicine.
A dual stream network for tumor detection in hyperspectral images
Weijtmans, P. J. C., Shan, C., Tan, T., de Koning, S. B., & Ruers, T. J. M. (2019, April). In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 1256-1259). IEEE.
When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?
Baltruschat, I. M., Steinmeister, L., Ittrich, H., Adam, G., Nickisch, H., Saalbach, A., ... & Knopp, T. (2019, April). In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 1362-1366). IEEE.
Artificial Intelligence in Clinical Health Care Applications
Van Hartskamp, M., Consoli, S., Verhaegh, W., Petkovic, M., & van de Stolpe, A. (2019). Interactive Journal of Medical Research, 8(2), e12100.
Context-Driven Concept Annotation in Radiology Reports: Anatomical Phrase Labeling
Zhu, H., Paschalidis, I. C., Hall, C., & Tahmasebi, A. (2019). AMIA Summits on Translational Science Proceedings, 2019, 232.
Learning deep similarity metric for 3D MR–TRUS image registration
Haskins, G., Kruecker, J., Kruger, U., Xu, S., Pinto, P. A., Wood, B. J., & Yan, P. (2019). International journal of computer assisted radiology and surgery, 14(3), 417-425.
Classification of pulmonary nodular findings based on characterization of change using radiology reports
Yuan, J., Zhu, H., & Tahmasebi, A. (2019). AMIA Summits on Translational Science Proceedings, 2019, 285.
CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications
Zavala-Mondragon, L. A., Lamichhane, B., Zhang, L., & de Haan, G. (2019). Journal of Ambient Intelligence and Humanized Computing, 1-12.
Deep-learning-based motion artefact measures for coronary CT angiography images
Lossau, T., Vembar, M., Nickisch, H., Wissel, T., Bippus, R. D., Morlock, M., & Grass, M. (2019, January). European Congress of Radiology 2019.
Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning
Tahmasebi, A. M., Zhu, H., Mankovich, G., Prinsen, P., Klassen, P., Pilato, S., ... & Chang, P. (2019). Journal of digital imaging, 32(1), 6-18.
Data science in healthcare: Benefits, challenges and opportunities
Abedjan, Z., Boujemaa, N., Campbell, S., Casla, P., Chatterjea, S., Consoli, S., ... & Hamelinck, D. (2019). In Data Science for Healthcare (pp. 3-38). Springer, Cham.
Introduction to Classification Algorithms and Their Performance Analysis Using Medical Examples
Korst, J., Pronk, V., Barbieri, M., & Consoli, S. (2019). In Data Science for Healthcare (pp. 39-73). Springer, Cham.
Safety and Regulatory Aspects of Systems for Disease Pre-Screening
Mohammad, S. (2019). In Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention (pp. 321-344). IGI Global.
Electrocardiogram Monitoring and Interpretation: From Traditional Machine Learning to Deep Learning, and Their Combination
Parvaneh, S., & Rubin, J. (2018, September). In 2018 Computing in Cardiology Conference (CinC) (Vol. 45, pp. 1-4). IEEE.
Organ-At-Risk Segmentation in Brain MRI Using Model-Based Segmentation: Benefits of Deep Learning-Based Boundary Detectors
Orasanu, E., Brosch, T., Glide-Hurst, C., & Renisch, S. (2018, September). In International Workshop on Shape in Medical Imaging (pp. 291-299). Springer, Cham.
Deep learning-based boundary detection for model-based segmentation with application to MR prostate segmentation
Brosch, T., Peters, J., Groth, A., Stehle, T., & Weese, J. (2018, September). In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 515-522). Springer, Cham.
Four-Dimensional ASL MR Angiography Phantoms with Noise Learned by Neural Styling
Phellan, R., Linder, T., Helle, M., Spina, T. V., Falcão, A., & Forkert, N. D. (2018). In Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (pp. 131-139). Springer, Cham.
Improving Clinical Subjects Clustering by Learning and Optimizing Feature Weights
Consoli, S., Hendriks, M., Vos, P., Kustra, J., Mavroeidis, D., & Hoffmann, R. (2018, September). In International Conference on Machine Learning, Optimization, and Data Science (pp. 305-316). Springer, Cham.
Attention-based medical caption generation with image modality classification and clinical concept mapping
Hasan, S. A., Ling, Y., Liu, J., Sreenivasan, R., Anand, S., Arora, T. R., ... & Farri, O. (2018, September). In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 224-230). Springer, Cham.
Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation
Parvaneh, S., Rubin, J., Rahman, A., Conroy, B., & Babaeizadeh, S. (2018). Physiological measurement, 39(8), 084003.
Methodologies for workforce optimization in Hospital's Emergency Department
Paul, S., Krishnamoorthy, P., Dinesh, M. S., Kailash, S., Bussa, N., & Mariyanna, S. (2018, July). In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4050-4053). IEEE.
Recurrent Neural Network for Classification of Snoring and Non-Snoring Sound Events
Arsenali, B., van Dijk, J., Ouweltjes, O., den Brinker, B., Pevernagie, D., Krijn, R., ... & Overeem, S. (2018, July). In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 328-331). IEEE.
Active learning experiments for the classification of smoking tweets
Härmä, A., Polyakov, A., & Chernyak, E. (2018). In AIH@ IJCAI (pp. 193-203).
Active Learning for Conversational Interfaces in Healthcare Applications
Härmä, A., Polyakov, A., & Artemova, E. (2018, July). In International Workshop on Artificial Intelligence in Health (pp. 48-58). Springer, Cham.
Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation
Wenzel, F., Meyer, C., Stehle, T., Peters, J., Siemonsen, S., Thaler, C., ... & Alzheimer’s Disease Neuroimaging Initiative. (2018). Medical image analysis, 46, 146-161.
Clothing Change Aware Person Identification
Xue, J., Meng, Z., Katipally, K., Wang, H., & van Zon, K. (2018). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 2112-2120).
A review of recent advances in data analytics for post-operative patient deterioration detection
Petit, C., Bezemer, R., & Atallah, L. (2018). Journal of clinical monitoring and computing, 32(3), 391-402.
Foveal fully convolutional nets for multi-organ segmentation
Brosch, T., & Saalbach, A. (2018, March). In Medical Imaging 2018: Image Processing (Vol. 10574, p. 105740U). International Society for Optics and Photonics.
Nearest neighbor 3D segmentation with context features
Hristova, E., Schulz, H., Brosch, T., Heinrich, M. P., & Nickisch, H. (2018, March). In Medical Imaging 2018: Image Processing (Vol. 10574, p. 105740M). International Society for Optics and Photonics.
Orientation regression in hand radiographs: a transfer learning approach
Baltruschat, I. M., Saalbach, A., Heinrich, M. P., Nickisch, H., & Jockel, S. (2018, March). In Medical Imaging 2018: Image Processing (Vol. 10574, p. 105741W). International Society for Optics and Photonics.
Spine centerline extraction and efficient spine reading of MRI and CT data
Lorenz, C., Vogt, N., Börnert, P., & Brosch, T. (2018, March). In Medical Imaging 2018: Image Processing (Vol. 10574, p. 1057425). International Society for Optics and Photonics.
Unsupervised Time Series Data Analysis for Error Pattern Extraction for Predictive Maintenance
Ravi, V., & Patil, R. (2018, April). In International Conference on Advances in Computing and Data Sciences (pp. 1-10). Springer, Singapore.
Deep-learning-based CT motion artifact recognition in coronary arteries
Elss, T., Nickisch, H., Wissel, T., Schmitt, H., Vembar, M., Morlock, M., & Grass, M. (2018, March). In Medical Imaging 2018: Image Processing (Vol. 10574, p. 1057416). International Society for Optics and Photonics.
Living-skin classification via remote-ppg
Wang, W., Stuijk, S., & de Haan, G. (2017). IEEE Transactions on Biomedical Engineering, 64(12), 2781-2792.
Cloud based big data platform for image analytics
Vuppala, S. K., Dinesh, M. S., Viswanathan, S., Ramachandran, G., Bussa, N., & Geetha, M. (2017, November). In 2017 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 11-18). IEEE.
Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings
Rubin, J., Parvaneh, S., Rahman, A., Conroy, B., & Babaeizadeh, S. (2017, September). In 2017 Computing in Cardiology (CinC) (pp. 1-4). IEEE.
Deep Learning Based Rib Centerline Extraction and Labeling
Lenga, M., Klinder, T., Bürger, C., von Berg, J., Franz, A., & Lorenz, C. (2018, September). In International Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging (pp. 99-113). Springer, Cham.
Automatic characterization of sleep need dissipation using a single hidden layer neural network
Garcia-Molina, G., Baehr, K., Steele, B., Tsoneva, T., Pfundtner, S., Riedner, B., ... & Tononi, G. (2017). In 2017 25th European Signal Processing Conference (EUSIPCO) (pp. 1305-1308). IEEE.
Non-wearable sensor based approach to monitor primary health conditions
Patil, R. B., & Krishnamoorthy, P. (2017, July). In 2017 IEEE Region 10 Symposium (TENSYMP) (pp. 1-4). IEEE.
Predictive modeling for corrective maintenance of imaging devices from machine logs
Patil, R. B., Patil, M. A., Ravi, V., & Naik, S. (2017, July). In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1676-1679). IEEE.
Neural clinical paraphrase generation with attention
Hasan, S. A., Liu, B., Liu, J., Qadir, A., Lee, K., Datla, V., ... & Farri, O. (2016, December). In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP) (pp. 42-53).
Clinical Question Answering using Key-Value Memory Networks and Knowledge Graph.
Hasan, S. A., Zhao, S., Datla, V. V., Liu, J., Lee, K., Qadir, A., ... & Farri, O. (2016). In TREC.
Four challenges in medical image analysis from an industrial perspective
Weese, J., & Lorenz, C. (2016).
Systolic blood pressure estimation using PPG and ECG during physical exercise
Sun, S., Bezemer, R., Long, X., Muehlsteff, J., & Aarts, R. M. (2016). Physiological measurement, 37(12), 2154.
A method to detect tortuosity of vessel using non imaging ultrasound approach in carotid structure
Patil, R. B., & Krishnamoorthy, P. (2016, August). In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3555-3559). IEEE.
An analytics based clinical decision support system for CVD risk assessment and management
Anand, S., Patil, R. B., & Krishnamoorthy, P. (2016, August). In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5620-5623). IEEE.
Automatic coronary lumen segmentation with partial volume modeling improves lesions' hemodynamic significance assessment
Freiman, M., Lamash, Y., Gilboa, G., Nickisch, H., Prevrhal, S., Schmitt, H., ... & Goshen, L. (2016, March). In Medical Imaging 2016: Image Processing (Vol. 9784, p. 978403). International Society for Optics and Photonics.
A method for localized computation of Pulse Wave Velocity in carotid structure
Patil, R. B., Krishnamoorthy, P., & Sethuraman, S. (2015, August). In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1898-1901). IEEE.
Fast Kronecker inference in Gaussian processes with non-Gaussian likelihoods
Flaxman, S., Wilson, A., Neill, D., Nickisch, H., & Smola, A. (2015, June). In International Conference on Machine Learning (pp. 607-616).
Learning patient-specific lumped models for interactive coronary blood flow simulations
Nickisch, H., Lamash, Y., Prevrhal, S., Freiman, M., Vembar, M., Goshen, L., & Schmitt, H. (2015, October). In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 433-441). Springer, Cham.
Temporal and spectral analysis of internal carotid artery Doppler signal for normal and abnormal flow detection
Krishnamoorthy, P., Patil, R. B., & Ravi, V. (2015, August). In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6122-6125). IEEE.
