4 citations · 11 across the 12 of their papers we have counts for
8 papers · 1 filter
LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentation
Vasileios Magoulianitis, Catherine A. Alexander, Jiaxin Yang +1
Nuclei segmentation is the cornerstone task in histology image reading, shedding light on the underlying molecular patterns and leading to disease or cancer diagnosis. Yet, it is a…
GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI
Jiaxin Yang, Vasileios Magoulianitis, Catherine Aurelia Christie Alexander +8
Prostate and zonal segmentation is a crucial step for clinical diagnosis of prostate cancer (PCa). Computer-aided diagnosis tools for prostate segmentation are based on the deep le…
PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation
Yijing Yang, Vasileios Magoulianitis, Jiaxin Yang +8
Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on d…
PCa-RadHop: A Transparent and Lightweight Feed-forward Method for Clinically Significant Prostate Cancer Segmentation
Vasileios Magoulianitis, Jiaxin Yang, Yijing Yang +8
Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increa…
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
Vasileios Magoulianitis, Catherine A. Alexander, C. -C. Jay Kuo
In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resol…
LGSQE: Lightweight Generated Sample Quality Evaluatoin
Ganning Zhao, Vasileios Magoulianitis, Suya You +1
Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample. To address this problem, a lightwe…