4 citations · 11 across the 12 of their papers we have counts for
12 papers
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…
RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection
Vasileios Magoulianitis, Jiaxin Yang, Catherine A. Alexander +1
Clinically significant prostate cancer (csPCa) is a leading cause of cancer death in men, yet it has a high survival rate if diagnosed early. Bi-parametric MRI (bpMRI) reading has…
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…
Learning Scene Flow With Skeleton Guidance For 3D Action Recognition
Vasileios Magoulianitis, Athanasios Psaltis
Among the existing modalities for 3D action recognition, 3D flow has been poorly examined, although conveying rich motion information cues for human actions. Presumably, its suscep…
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…
Statistical Attention Localization (SAL): Methodology and Application to Object Classification
Yijing Yang, Vasileios Magoulianitis, Xinyu Wang +1
A statistical attention localization (SAL) method is proposed to facilitate the object classification task in this work. SAL consists of three steps: 1) preliminary attention windo…
HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation
Vasileios Magoulianitis, Yijing Yang, C. -C. Jay Kuo
A high-performance unsupervised nuclei instance segmentation (HUNIS) method is proposed in this work. HUNIS consists of two-stage block-wise operations. The first stage includes: 1…
Unsupervised Data-Driven Nuclei Segmentation For Histology Images
Vasileios Magoulianitis, Peida Han, Yijing Yang +1
An unsupervised data-driven nuclei segmentation method for histology images, called CBM, is proposed in this work. CBM consists of three modules applied in a block-wise manner: 1)…
E-PixelHop: An Enhanced PixelHop Method for Object Classification
Yijing Yang, Vasileios Magoulianitis, C. -C. Jay Kuo
Based on PixelHop and PixelHop++, which are recently developed using the successive subspace learning (SSL) framework, we propose an enhanced solution for object classification, ca…