54 citations · 239 across the 30 of their papers we have counts for
14 papers · 1 filter
Atlas of MICCAI: 28 Years of Representation, Collaboration, and the Road to Inclusion
Amelia Jiménez-Sánchez, Laura Arbelaez Ossa, Joan Perramon-Llussà +6
Scientific knowledge in medical image analysis is shaped by venues such as the Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference, which influence resea…
Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images
Sebastian Ibarra, Javier del Riego, Alessandro Catanese +7
Dynamic contrast-enhanced (DCE) MRI is essential for breast cancer diagnosis and treatment. However, its reliance on contrast agents introduces safety concerns, contraindications,…
Clinically-guided Data Synthesis for Laryngeal Lesion Detection
Chiara Baldini, Kaisar Kushibar, Richard Osuala +4
Although computer-aided diagnosis (CADx) and detection (CADe) systems have made significant progress in various medical domains, their application is still limited in specialized f…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou +6
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.…
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Richard Osuala, Daniel M. Lang, Preeti Verma +9
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization…
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou +4
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, incl…
Revisiting Skin Tone Fairness in Dermatological Lesion Classification
Thorsten Kalb, Kaisar Kushibar, Celia Cintas +3
Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones. However, the absence of skin t…
medigan: a Python library of pretrained generative models for medical image synthesis
Richard Osuala, Grzegorz Skorupko, Noussair Lazrak +9
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging. However, there is (1) limited ava…
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Lidia Garrucho, Kaisar Kushibar, Richard Osuala +7
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance sinc…
Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification
Zuzanna Szafranowska, Richard Osuala, Bennet Breier +3
Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortali…
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation
Kaisar Kushibar, Víctor Manuel Campello, Lidia Garrucho Moras +3
Uncertainty estimation in deep learning has become a leading research field in medical image analysis due to the need for safe utilisation of AI algorithms in clinical practice. Mo…
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study
Lidia Garrucho, Kaisar Kushibar, Socayna Jouide +3
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural netwo…
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
Akis Linardos, Kaisar Kushibar, Sean Walsh +2
Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis stu…