54 citations · 239 across the 30 of their papers we have counts for
14 papers · 1 filter
Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities
Nick Lemke, Niklas Ihm, John Kalkhof +11
Neural Cellular Automata (NCAs) are a new type of neural network architecture which enable accurate and robust inference at extremely small model sizes. Recently, NCAs have advance…
Dense Temporal Contrast Synthesis via Conditioned Latent Transport
Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang +15
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in…
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
Johannes Kiechle, Richard Osuala, Daniel M. Lang +5
In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated…
A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy +5
Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition b…
Med-DualLoRA: Local Adaptation of Foundation Models for 3D Cardiac MRI
Joan Perramon-Llussà, Amelia Jiménez-Sánchez, Grzegorz Skorupko +4
Foundation models (FMs) show great promise for robust downstream performance across medical imaging tasks and modalities, including cardiac magnetic resonance (CMR), following task…
The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
Lidia Garrucho, Smriti Joshi, Kaisar Kushibar +43
Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imagin…
Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation
Stefan M. Fischer, Johannes Kiechle, Laura Daza +6
In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the…
Mask-Guided Multi-Channel SwinUNETR Framework for Robust MRI Classification
Smriti Joshi, Lidia Garrucho, Richard Osuala +2
Breast cancer is one of the leading causes of cancer-related mortality in women, and early detection is essential for improving outcomes. Magnetic resonance imaging (MRI) is a high…
Single Image Test-Time Adaptation via Multi-View Co-Training
Smriti Joshi, Richard Osuala, Lidia Garrucho +4
Test-time adaptation enables a trained model to adjust to a new domain during inference, making it particularly valuable in clinical settings where such on-the-fly adaptation is re…
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas +29
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key…
Federated nnU-Net for Privacy-Preserving Medical Image Segmentation
Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla +11
The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, a…
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Richard Osuala, Daniel M. Lang, Anneliese Riess +6
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
Mitigating annotation shift in cancer classification using single image generative models
Marta Buetas Arcas, Richard Osuala, Karim Lekadir +1
Artificial Intelligence (AI) has emerged as a valuable tool for assisting radiologists in breast cancer detection and diagnosis. However, the success of AI applications in this dom…