Irina Voiculescu
Associate Professor of Computer Science
Leads the OxMedIS group developing machine learning for medical image segmentation, 3D reconstruction and landmark detection.
Irina Voiculescu's lab at Oxford works on medical imaging & healthcare AI, 3D vision & neural rendering and recognition, detection & segmentation. 27 papers since 2023, 9 at top venues, mostly ISBI and MICCAI. 6 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 33%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 6
Papers by yearfrom the lab homepage, 2023–2026
- 8 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 3
- 6 papers2024 · T1 2
- 5 papers2023 · T1 3
T1 venuesOtherVenues: ISBI, MICCAI, ICCV, CVPR, ECCV
Research focuspaper titles that use each topic's words
- Recognition, detection & segmentation18 papers
- Medical imaging & healthcare AI4 papers
- image segmentation2 papers
- 3D vision & neural rendering0 papers
- developing machine0 papers
Paper list
From the lab homepage.
20268 papers
- ISBIAutomated landmark detection for assessing hip conditions: a cross−modality validation of MRI versus x−ray (opens in a new tab)
- MICCAI (T1)CDPM−Align: Multi−Scale Guidance−Aligned Diffusion Pretraining for Robust Few−Shot Anatomical Landmark Detection (opens in a new tab)
- Frontiers in Medical TechnologyFrom Landmark Uncertainty to Clinical Confidence: Screening for Developmental Dysplasia of the Hip (opens in a new tab)
- Efficient Medical Artificial IntelligenceHow Do Train−Time Pruning Dynamics and Pruning Schedules Affect Retinal Vessel Segmentation (opens in a new tab)
- ICIPPLESS: Pseudo−label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation (opens in a new tab)
- ML-CDS : Multimodal Learning and Fusion Across Scales for Clinical Decision SupportQuantifying Contributions within Multimodal Fusion for Clinical Decisions (opens in a new tab)
- Frontiers in ImagingThe hidden value of uncertainty in medical segmentation (opens in a new tab)
- UNSURE : Uncertainty for Safe Utilization of Machine Learning in Medical ImagingWhen Voxels Are Not Equal: Spacing−Driven Metric Bias and Metric−Uncertainty Decoupling in Segmentation (opens in a new tab)
20258 papers
- ICIAPA Handful of Data: Evaluating Few–Shot Incremental Landmark Detection (opens in a new tab)
- ICCV (T1)Are X−ray landmark detection models fair? A preliminary assessment and mitigation strategy (opens in a new tab)
- MICCAI (T1)Confidence in Angle Predictions for Clinical Decision Support (opens in a new tab)
- World Conference of Orthopaedic Research (WCOR). September, .Dynamic CT Kinematic Analysis in Femoroacetabular Impingement: A Proof−of−Concept (opens in a new tab)
- JPDCParallel Watershed Partitioning: GPU−Based Hierarchical Image Segmentation (opens in a new tab)
- The XXX Congress of the International Society of Biomechanics (ISB). July, .Proof−of−concept of kinematic analysis in FAI patients: a dinamic CT study (opens in a new tab)
- ICCV (T1)Quantifying inter−operator variability and its causes for medical semantic segmentation (opens in a new tab)
- Medical Image Understanding and Analysis (MIUA). July, .Semantic Segmentation with Spreading Scribbles (opens in a new tab)
20246 papers
- CVPR (T1)Hairy ground truth enhancement for semantic segmentation (opens in a new tab)
- Annual Conference on Medical Image Understanding and AnalysisImproving Automated Ultrasound Infant Hip Screening using an Integrated Clinical Classification Loss (opens in a new tab)
- ECCV (T1)Landmark−Based Screening: Femoral Head Coverage and Graf Classification in Infant Developmental Dysplasia of the Hip (opens in a new tab)
- Winner of the Landmark Detection Challenge at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). October, .Optimising for the Unknown: Domain Alignment for Cephalometric Landmark Detection (opens in a new tab)
- ISBIRuntime Freezing: Dynamic Class Loss for multi−organ 3D Segmentation (opens in a new tab)
- ISBISuper−TrustScore: Reliable Failure Detection for Automated Skin Lesion Diagnosis (opens in a new tab)
20235 papers
- Applied SciencesDealing with Unreliable Annotations: A Noise−Robust Network for Semantic Segmentation through A Transformer−Improved Encoder and Convolution Decoder (opens in a new tab)
- MICCAI (T1)Exigent Examiner and Mean Teacher: A Novel 3D CNN−based Semi−Supervised Learning Framework for Brain Tumor Segmentation (opens in a new tab)
- TMI (T1)Federated Partially Supervised Learning with Limited Decentralized Medical Images (opens in a new tab)
- ISBIScreening Infant Hips: DDH Diagnosis Via Deep Learning (opens in a new tab)
- MICCAI (T1)Weakly Supervised Medical Image Segmentation through Dense Combinations of Dense Pseudo−Labels (opens in a new tab)
Where alumni went6 listed
Apply to Oxford2027-28
DPhil in Computer Science
- Round
- 2027-28 entry (January round)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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