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AI publications

    Our latest scientific publications about our AI research methods and results

    Key publications

     

    Comparison of deep learning approaches for multi-label chest X-ray classification

    Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A. (2019). Scientific reports, 9(1), 6381.

     

    Kernel interpolation for scalable structured Gaussian processes (KISS-GP)

    Wilson, A., & Nickisch, H. (2015, June). In International Conference on Machine Learning (pp. 1775-1784).

     

    Deep Learning for Fast Adaptive Beamforming

    Luijten, B., Cohen, R., de Bruijn, F. J., Schmeitz, H. A., Mischi, M., Eldar, Y. C., & van Sloun, R. J. (2019, May). In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1333-1337). IEEE.

     

    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.

     

    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.

     

    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.

     

    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.

     

    Data Science for Healthcare: Methodologies and Applications

    Consoli, S. & Reforgiato Recupero, D. & Petkovic, M. (2019). 

     

     

    Other publications

     

    Validation of a high precision semantic search tool using a curated dataset containing related and unrelated reports of clinically relevant search terms

    Vijayananda, J., Kumar, N., & Shastry, A. (2020). Accepted for publication at ECR 2020

     

    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 Research8(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 Proceedings2019, 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 surgery14(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 Proceedings2019, 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 imaging32(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 measurement39(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.

     

    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 computing32(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 Engineering64(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).

     

    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).

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