5 citations · 13 across the 12 of their papers we have counts for
12 papers
SegReg: Latent Space Regularization for Improved Medical Image Segmentation
Puru Vaish, Amin Ranem, Felix Meister +3
Medical image segmentation models are typically optimised with voxel-wise losses that constrain predictions only in the output space. This leaves latent feature representations lar…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
SASVi -- Segment Any Surgical Video
Ssharvien Kumar Sivakumar, Yannik Frisch, Amin Ranem +1
Purpose: Foundation models, trained on multitudes of public datasets, often require additional fine-tuning or re-prompting mechanisms to be applied to visually distinct target doma…
NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation
Amin Ranem, John Kalkhof, Anirban Mukhopadhyay
Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt…
NCA-Morph: Medical Image Registration with Neural Cellular Automata
Amin Ranem, John Kalkhof, Anirban Mukhopadhyay
Medical image registration is a critical process that aligns various patient scans, facilitating tasks like diagnosis, surgical planning, and tracking. Traditional optimization bas…
Unsupervised Training of Neural Cellular Automata on Edge Devices
John Kalkhof, Amin Ranem, Anirban Mukhopadhyay
The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly…
Continual atlas-based segmentation of prostate MRI
Amin Ranem, Camila González, Daniel Pinto dos Santos +3
Continual learning (CL) methods designed for natural image classification often fail to reach basic quality standards for medical image segmentation. Atlas-based segmentation, a we…
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology
Amin Ranem, Niklas Babendererde, Moritz Fuchs +1
Medical imaging plays a critical role in the diagnosis and treatment planning of various medical conditions, with radiology and pathology heavily reliant on precise image segmentat…
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
Amin Ranem, John Kalkhof, Caner Özer +2
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR)…
Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Camila Gonzalez, Amin Ranem, Ahmed Othman +1
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explo…
Continual Hippocampus Segmentation with Transformers
Amin Ranem, Camila González, Anirban Mukhopadhyay
In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most e…
Quality monitoring of federated Covid-19 lesion segmentation
Camila Gonzalez, Christian Harder, Amin Ranem +5
Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fas…