1.3k citations · 1.9k across the 80 of their papers we have counts for
34 papers · 1 filter
Spatial Masked-Set Learning for Sparse Multi-Shell Diffusion MRI Signal Synthesis
Yousef Sadegheih, Pratibha Kumari, Dorit Merhof
Dense multi-shell diffusion MRI provides rich q-space information but requires long acquisition times. We propose a spatial masked-set framework for sparse multi-shell diffusion MR…
Towards Modality-Agnostic Continual Domain-Incremental Brain Lesion Segmentation
Yousef Sadegheih, Dorit Merhof, Pratibha Kumari
Brain lesion segmentation from multi-modal MRI often assumes fixed modality sets or predefined pathologies, making existing models difficult to adapt across cohorts and imaging pro…
Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI
Yousef Sadegheih, Dorit Merhof
Accurate brain parcellation in diffusion MRI (dMRI) space is essential for advanced neuroimaging analyses. However, most existing approaches rely on anatomical MRI for segmentation…
Modality-Agnostic Brain Lesion Segmentation with Privacy-aware Continual Learning
Yousef Sadegheih, Pratibha Kumari, Dorit Merhof
Traditional brain lesion segmentation models for multi-modal MRI are typically tailored to specific pathologies, relying on datasets with predefined modalities. Adapting to new MRI…
Echo-ENet: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation
Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia +6
Left ventricular ejection fraction (LVEF) is a primary marker of cardiac function. However, routine estimation from endocardial measurements requires manual delineation at end-dias…
Continual Domain Incremental Learning for Privacy-aware Digital Pathology
Pratibha Kumari, Daniel Reisenbüchler, Lucas Luttner +3
In recent years, there has been remarkable progress in the field of digital pathology, driven by the ability to model complex tissue patterns using advanced deep-learning algorithm…
Tissue Concepts: supervised foundation models in computational pathology
Till Nicke, Jan Raphael Schaefer, Henning Hoefener +4
Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Founda…
Physics-Inspired Generative Models in Medical Imaging: A Review
Dennis Hein, Afshin Bozorgpour, Dorit Merhof +1
Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging…
Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study
Yousef Sadegheih, Dorit Merhof
Brain metastasis segmentation poses a significant challenge in medical imaging due to the complex presentation and variability in size and location of metastases. In this study, we…
Unsupervised Latent Stain Adaptation for Computational Pathology
Daniel Reisenbüchler, Lucas Luttner, Nadine S. Schaadt +2
In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techn…
MSANet: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation
Sina Ghorbani Kolahi, Seyed Kamal Chaharsooghi, Toktam Khatibi +5
Medical image segmentation involves identifying and separating object instances in a medical image to delineate various tissues and structures, a task complicated by the significan…
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
Moein Heidari, Sina Ghorbani Kolahi, Sanaz Karimijafarbigloo +8
Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emerge…
LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation
Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari +2
The rise of Transformer architectures has advanced medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers. However,…
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Moein Heidari, Reza Azad, Sina Ghorbani Kolahi +8
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Comput…
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Pratibha Kumari, Joohi Chauhan, Afshin Bozorgpour +3
Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning al…
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Afshin Bozorgpour, Yousef Sadegheih, Amirhossein Kazerouni +2
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have rec…
Implicit Neural Representation in Medical Imaging: A Comparative Survey
Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli +4
Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing n…
A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
Yuli Wu, Ivan Karetic, Johannes Stegmaier +2
We propose a neural network-based framework to optimize the perceptions simulated by the in silico retinal implant model pulse2percept. The overall pipeline consists of a trainable…
Medical Image Segmentation Review: The success of U-Net
Reza Azad, Ehsan Khodapanah Aghdam, Amelie Rauland +7
Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most wi…
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari +4
Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward…
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
Ehsan Khodapanah Aghdam, Reza Azad, Maral Zarvani +1
Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diag…
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Reza Azad, Moein Heidari, Moein Shariatnia +4
Convolutional neural networks (CNNs) have been the de facto standard in a diverse set of computer vision tasks for many years. Especially, deep neural networks based on seminal arc…
Contextual Attention Network: Transformer Meets U-Net
Reza Azad, Moein Heidari, Yuli Wu +1
Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the de facto standard and attained immense success in medical image segmentation. However, as a downside, C…
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
Reza Azad, Nika Khosravi, Mohammad Dehghanmanshadi +2
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. The combination of a s…