Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches

Andjela DimitrijevicNeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada
Research Center, Ste-Justine Hospital University Centre, Montréal, QC, Canada, Vincent NobletICube-UMR 7357, Université de Strasbourg, CNRS, Strasbourg, France, Benjamin De LeenerNeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada
Research Center, Ste-Justine Hospital University Centre, Montréal, QC, Canada
Computer Engineering and Software Engineering, Polytechnique Montréal, Montréal, QC, Canada

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation

Yidong ZhaoDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands, João TouraisDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands, Iain PierceBarts Heart Centre, Barts Health NHS Trust, London, United Kingdom
Institute of Cardiovascular Science, University College, London, United Kingdom, Christian NitscheBarts Heart Centre, Barts Health NHS Trust, London, United Kingdom
Institute of Cardiovascular Science, University College, London, United Kingdom, Thomas A. TreibelBarts Heart Centre, Barts Health NHS Trust, London, United Kingdom
Institute of Cardiovascular Science, University College, London, United Kingdom, Sebastian WeingärtnerDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands, Artur M. SchweidtmannDepartment of Chemical Engineering, Delft University of Technology, Delft, The Netherlands, Qian TaoDepartment of Imaging Physics, Delft University of Technology, Delft, The Netherlands

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge

Stefan M. FischerSchool of Computation, Information and Technology, Technical University Munich, Germany
Department of RadioOncology, Klinikum rechts der Isar, Technical University Munich, Germany
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany
Munich Center of Machine Learning (MCML), Germany, Johannes KiechleSchool of Computation, Information and Technology, Technical University Munich, Germany
Department of RadioOncology, Klinikum rechts der Isar, Technical University Munich, Germany
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany
Munich Center of Machine Learning (MCML), Germany, Daniel M. LangSchool of Computation, Information and Technology, Technical University Munich, Germany
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany, Jan C. PeekenDepartment of RadioOncology, Klinikum rechts der Isar, Technical University Munich, Germany, Julia A. SchnabelSchool of Computation, Information and Technology, Technical University Munich, Germany
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany
Munich Center of Machine Learning (MCML), Germany
School of Biomedical Engineering and Imaging Sciences, King’s College London, UK

Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations

Akshay V GaikwadComputer Science and Enginnering, Indian Institute of Technology Bombay, Suyash AwateComputer Science and Enginnering, Indian Institute of Technology Bombay

LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas

Sofija EngelsonInstitute of Medical Informatics, University of Lübeck, Jan EhrhardtInstitute of Medical Informatics, University of Lübeck
German Research Center for Artificial Intelligence, Timo KeppGerman Research Center for Artificial Intelligence, Joshua NiemeijerGerman Aerospace Center, Heinz HandelsInstitute of Medical Informatics, University of Lübeck
German Research Center for Artificial Intelligence

Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data

Ivan DiazUniversity Institute of Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University Hospital of Bern, Mario GeigerNvidia (United States), Richard Iain McKinleyUniversity Institute of Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), University Hospital of Bern

A voxel-level approach to brain age prediction: A method to assess regional brain aging

Neha GianchandaniDepartment of Biomedical Engineering, University of Calgary
Hotchkiss Brain Institute, University of Calgary, Mahsa DibajiDepartment of Electrical and Software Engineering, University of Calgary, Johanna OspelDepartment of Radiology, University of Calgary
Department of Clinical Neurosciences, University of Calgary, Fernando VegaDepartment of Biomedical Engineering, University of Calgary, Mariana BentoDepartment of Biomedical Engineering, University of Calgary
Department of Electrical and Software Engineering, University of Calgary
Hotchkiss Brain Institute, University of Calgary, M. Ethan MacDonaldDepartment of Biomedical Engineering, University of Calgary
Department of Electrical and Software Engineering, University of Calgary
Department of Radiology, University of Calgary
Hotchkiss Brain Institute, University of Calgary, Roberto SouzaDepartment of Electrical and Software Engineering, University of Calgary
Hotchkiss Brain Institute, University of Calgary

HyperPredict: Estimating Hyperparameter Effects for Instance-Specific Regularization in Deformable Image Registration

Aisha Lawal ShuaibuInformatics, University of Sussex, Ivor J. A. SimpsonInformatics, University of Sussex

CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation

Yunjie ChenDepartment of Radiology, Leiden University Medical Center, Marius StaringDepartment of Radiology, Leiden University Medical Center, Olaf M. NeveDepartment of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, Stephan R. RomeijnDepartment of Radiology, Leiden University Medical Center, Erik F. HensenDepartment of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, Berit M. VerbistDepartment of Radiology, Leiden University Medical Center, Jelmer M. WolterinkDepartment of Applied Mathematics, Technical Medical Center, University of Twente, Qian TaoDepartment of Imaging Physics, Delft University of Technology

Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

Ravi HassanalySorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France, Camille BrianceauSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France, Maëlys SolalSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France, Olivier ColliotSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France, Ninon BurgosSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France

Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation

Sean C. EpsteinDepartment of Computer Science, University College London
Centre for Medical Image Computing, University College London, Timothy J. P. BrayCentre for Medical Imaging, University College London, Margaret Hall-CraggsCentre for Medical Imaging, University College London, Hui ZhangDepartment of Computer Science, University College London
Centre for Medical Image Computing, University College London

Score-Based Generative Models for PET Image Reconstruction

Imraj RD SinghDepartment of Computer Science, University College London, Alexander DenkerCenter for Industrial Mathematics, University of Bremen, Riccardo BarbanoDepartment of Computer Science, University College London, Željko KeretaDepartment of Computer Science, University College London, Bangti JinDepartment of Mathematics, Chinese University of Hong Kong, Kris ThielemansInstitute of Nuclear Medicine, University College London, Peter MaassCenter for Industrial Mathematics, University of Bremen, Simon ArridgeDepartment of Computer Science, University College London

Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series

S. Mazdak AbulnagaCSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA
MGH/HST Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA, Neel DeyCSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA, Sean I. YoungMGH/HST Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA
CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA, Eileen PanCSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA, Katherine I. HobgoodCSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA, Clinton J. WangCSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA, P. Ellen GrantFetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA, Esra Abaci TurkFetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA, Polina GollandCSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA

A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models

Yunguan FuUniversity College London, UK
InstaDeep, UK, Yiwen LiUniversity of Oxford, UK, Shaheer U. SaeedUniversity College London, UK, Matthew J. ClarksonUniversity College London, UK, Yipeng HuUniversity College London, UK

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

Paula RamirezBiomedical Engineering and Imaging Sciences, King's College London, Alena UusBiomedical Engineering and Imaging Sciences, King's College London, Milou P.M. van PoppelBiomedical Engineering and Imaging Sciences, King's College London, Irina GrigorescuBiomedical Engineering and Imaging Sciences, King's College London, Johannes K. SteinwegBiomedical Engineering and Imaging Sciences, King's College London, David F.A. LloydBiomedical Engineering and Imaging Sciences, King's College London, Kuberan PushparajahBiomedical Engineering and Imaging Sciences, King's College London, Andrew P. KingBiomedical Engineering and Imaging Sciences, King's College London, Maria DeprezBiomedical Engineering and Imaging Sciences, King's College London

Towards Early Prediction of Human iPSC Reprogramming Success

Abhineet SinghDepartment of Computing Science, University of Alberta, Ila JasraAlberta Diabetes Institute, University of Alberta, Omar MouhammedAlberta Diabetes Institute, University of Alberta, Nidheesh DadheechAlberta Diabetes Institute, University of Alberta, Nilanjan RayDepartment of Computing Science, University of Alberta, James ShapiroAlberta Diabetes Institute, University of Alberta

Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)

Dimitri HamzaouiUniversité Côte d’Azur, Inria, Epione Team, Sophia Antipolis, France, Sarah MontagneAcademic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France, Raphaële Renard-PennaAcademic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
GRC 5 Predictive Onco-Urology, Sorbonne University, Paris, France, Nicholas AyacheUniversité Côte d’Azur, Inria, Epione Team, Sophia Antipolis, France, Hervé DelingetteUniversité Côte d’Azur, Inria, Epione Team, Sophia Antipolis, France

Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

Amirhossein RasoulianDepartment of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada, Soorena SalariDepartment of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada, Yiming XiaoDepartment of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization

Mara GrazianiUniversity of Applied Sciences Western Switzerland (HES-SO Valais), 3960, Sierre, Switzerland
University of Geneva (UNIGE), Department of Computer Science (CUI), 1227, Carouge, Switzerland, Sebastian OtáloraUniversity of Applied Sciences Western Switzerland (HES-SO Valais), 3960, Sierre, Switzerland
University of Geneva (UNIGE), Department of Computer Science (CUI), 1227, Carouge, Switzerland, Stéphane Marchand-MailletUniversity of Applied Sciences Western Switzerland (HES-SO Valais), 3960, Sierre, Switzerland, Henning MüllerUniversity of Applied Sciences Western Switzerland (HES-SO Valais), 3960, Sierre, Switzerland
University of Geneva (UNIGE), Department of Computer Science (CUI), 1227, Carouge, Switzerland, Vincent AndrearczykUniversity of Applied Sciences Western Switzerland (HES-SO Valais), 3960, Sierre, Switzerland

Bayesian Optimization of Sampling Densities in MRI

Alban GossardInstitut de Mathématiques de Toulouse (IMT); UMR5219; Université de Toulouse; CNRS, France
Université Paul Sabatier, F-31062 Toulouse Cedex 9, France, Frédéric de GournayInstitut de Mathématiques de Toulouse (IMT); UMR5219; Université de Toulouse; CNRS, France
INSA de Toulouse; F-31077 Toulouse, France, Pierre WeissInstitut de Mathématiques de Toulouse (IMT); UMR5219; Université de Toulouse; CNRS, France
Université de Toulouse, CNRS
Centre de Biologie Intégrative (CBI), Laboratoire MCD, F-31062 Toulouse Cedex, France

Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs

Sina AmirrajabDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands, Yasmina Al KhalilDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands, Cristian LorenzPhilips Research Laboratories, Hamburg, Germany, Jürgen WeesePhilips Research Laboratories, Hamburg, Germany, Josien PluimDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands, Marcel BreeuwerDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Philips Healthcare, MR R&D - Clinical Science, Best, The Netherlands

Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness

Farnaz Khun JushTechnology Excellence, Siemens Healthcare GmbH, Erlangen, Germany
Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen, Germany, Markus BieleTechnology Excellence, Siemens Healthcare GmbH, Erlangen, Germany, Peter M. DueppenbeckerTechnology Excellence, Siemens Healthcare GmbH, Erlangen, Germany, Andreas MaierPattern Recognition Lab, Friedrich-Alexander-University, Erlangen, Germany

Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient

Effect of latent space distribution on the segmentation of images with multiple annotations

Ishaan BhatImage Sciences Institute, University Medical Center Utrecht, The Netherlands, Josien P.W. PluimDepartment of Biomedical Engineering, Eindhoven University of Technology, The Netherlands, Max A. ViergeverImage Sciences Institute, University Medical Center Utrecht, The Netherlands, Hugo J. KuijfImage Sciences Institute, University Medical Center Utrecht, The Netherlands

Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis

Xiao QiDepartment of Electrical and Computer Engineering, Rutgers Univeristy, New Brunswick, NJ, USA, David J. ForanRutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA, John L. NosherDepartment of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA, Ilker HacihalilogluDepartment of Radiology, The University of British Columbia, BC, Canada
Department of Medicine, The University of British Columbia, BC, Canada

Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection

Ashay PatelKing’s College London, Petru-Danial TudiosuKing’s College London, Walter H.L. PinayaKing’s College London, Gary CookKing’s College London, Vicky GohKing’s College London, Sebastien OurselinKing’s College London, M. Jorge CardosoKing’s College London

Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey

Jérôme RonyLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada, Soufiane BelharbiLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada, Jose DolzLIVIA, Dept. of Software and IT Engineering, École de technologie supérieure, Montreal, Canada, Ismail Ben AyedLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada, Luke McCaffreyGoodman Cancer Research Centre, Dept. of Oncology, McGill University, Montreal, Canada, Eric GrangerLIVIA, Dept. of Systems Engineering, École de technologie supérieure, Montreal, Canada

Focused Decoding Enables 3D Anatomical Detection by Transformers

Bastian WittmannDepartment of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, Fernando NavarroDepartment of Informatics, Technical University of Munich, Munich, Germany, Suprosanna ShitDepartment of Informatics, Technical University of Munich, Munich, Germany, Bjoern MenzeDepartment of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland

Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery