7 citations · 23 across the 27 of their papers we have counts for
6 papers · 1 filter
Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
Ashkan Shakarami, Azade Farshad, Yousef Yeganeh +4
We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This a…
Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structures
Yousef Yeganeh, Rui Xiao, Goktug Guvercin +2
While conventional computer vision emphasizes pixel-level and feature-based objectives, medical image analysis of intricate biological structures necessitates explicit representati…
Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis
Sven Lüpke, Yousef Yeganeh, Ehsan Adeli +2
Recent advances in generative models for medical imaging have shown promise in representing multiple modalities. However, the variability in modality availability across datasets l…
Shape-Aware Masking for Inpainting in Medical Imaging
Yousef Yeganeh, Azade Farshad, Nassir Navab
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independen…
Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation
Azade Farshad, Yousef Yeganeh, Peter Gehlbach +1
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize th…
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation
Anastasia Makarevich, Azade Farshad, Vasileios Belagiannis +1
The lack of sufficient annotated image data is a common issue in medical image segmentation. For some organs and densities, the annotation may be scarce, leading to poor model trai…