Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Elhabian, Shireen Y"'
This paper introduces a Virtual Immunohistochemistry Multiplex staining (VIMs) model designed to generate multiple immunohistochemistry (IHC) stains from a single hematoxylin and eosin (H&E) stained tissue section. IHC stains are crucial in pathology
Externí odkaz:
http://arxiv.org/abs/2407.19113
Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphology-based diagnostics and surgical planning. However, the effectivenes
Externí odkaz:
http://arxiv.org/abs/2407.15260
Autor:
Iyer, Krithika, Elhabian, Shireen Y.
The study of physiology demonstrates that the form (shape)of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical research. Statistical shape modeling (SSM) is a widely used tool for qua
Externí odkaz:
http://arxiv.org/abs/2407.01931
Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements in medical research, diagnostics, and treatment planning. Traditional m
Externí odkaz:
http://arxiv.org/abs/2404.17967
Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of a
Externí odkaz:
http://arxiv.org/abs/2403.12290
Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis and tumor recurrence tracking. Hematoxylin excels at highlighting nuclei, whereas eosin stains the cytoplasm. However, H&E stain lacks details for differentiating di
Externí odkaz:
http://arxiv.org/abs/2403.11340
Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despit
Externí odkaz:
http://arxiv.org/abs/2403.11026
Statistical Shape Modeling (SSM) effectively analyzes anatomical variations within populations but is limited by the need for manual localization and segmentation, which relies on scarce medical expertise. Recent advances in deep learning have provid
Externí odkaz:
http://arxiv.org/abs/2403.11008
Publikováno v:
Shape in Medical Imaging (ShapeMI 2023), p47_54, Springer Nature Switzerland
Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific morphological
Externí odkaz:
http://arxiv.org/abs/2401.00067
With the advent of digital scanners and deep learning, diagnostic operations may move from a microscope to a desktop. Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading, but pat
Externí odkaz:
http://arxiv.org/abs/2308.13182