Welcome to Predictive Intelligence and Medical Imaging (PIMI) Laboratory
In ITU PIMI Lab, our goal is to design and realize artificial intelligence algorithms dedicated to medical imaging, computer vision and predictive models. Our current research focuses on machine learning and deep learning, with a focus on medical image quality assessment, medical image reconstruction and electricity price forecasting.
Recent Publications
Karagoz, A., Alis, D., Seker, M.E, Zeybel G., Yergin M., Oksuz, I., Karaarslan, E., Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study. Insights Imaging, 2023. Online
Alis, D., Kartal, M. S., Seker, M. E., Guroz, B., Basar, Y., Arslan, A.,Sirolu S., Kurtcan S., Denizoglu N., Tuzun U., Yldrm D., Oksuz, I., Karaarslan, E.. Deep Learning for Assessing Image Quality in Bi-Parametric Prostate MRI: A Feasibility Study. European Journal of Radiology 2023. Online Bohlender S., Oksuz, I., Mukhopadhyay A., A survey on shape-constraint deep learning for medical image segmentation, IEEE Reviews in Biomedical Engineering, 2023. Online Arxiv
Soyak R., Ersoy E.A., Navruz E., Cruz G., Prieto C., King A.P., Unay D., Oksuz, I., Channel Attention Networks for Robust MR Fingerprint Matching, IEEE Transactions on Biomedical Engineering 2021. Online
Gunduz S., Ugurlu U., Oksuz, I., Transfer Learning for Electricity Price Forecasting, Sustainable Energy, Grids and Networks 2023. Arxiv Online
Ozer C., Oksuz, I., Explainable Image Quality Analysis of Chest X-Rays, MIDL (oral acceptance rate < %15), 2021 (oral presentation). Online , Code
Oksuz, I., Brain MRI artefact detection and correction using convolutional neural networks, Computer Methods and Programs in Biomedicine(IF: 3.632), 2021, Online
Oksuz, I., Clough J., Ruijsink B., Puyol-Anton E., Bustin A., Cruz G.,, Prieto C., King A.P., Schnabel J.A., Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation, IEEE TMI (accepted, IF: 7.816), 2020. Online
Clough J., Oksuz, I., Bryne N., Schnabel J.A., King A.P., Explicit topological priors for deep-learning based image segmentation using persistent homology, IPMI (acceptance rate < %25), 2019. Online
Oksuz, I., Ruijsink B., Puyol-Anton E., Clough J., Cruz G., Bustin A., Botnar R., Prieto C., Rueckert D., Schnabel J.A., King A.P., Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning, Medical Image Analysis (IF: 8.880), 2019. Online