1 citations · 1 across the 4 of their papers we have counts for
4 papers
Clinical Feasibility of Low-Magnification Fluorescence Imaging for Breast Cancer Margin Detection Using Texture Analysis and Deep Learning
Pouya Afshin, Tianling Niu, Tongtong Lu +6
High-resolution images of unprocessed surgical breast tissue can be obtained using microscopy with ultraviolet surface excitation (MUSE). This technique is considered a promising m…
Self-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images
Pouya Afshin, David Helminiak, Tianling Niu +4
Breast-Conserving Surgery (BCS) requires precise intraoperative margin assessment to preserve healthy tissue. Deep Ultraviolet Fluorescence Scanning Microscopy (DUV-FSM) offers rap…
Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework
Pouya Afshin, David Helminiak, Tongtong Lu +5
Breast-conserving surgery (BCS) aims to completely remove malignant lesions while maximizing healthy tissue preservation. Intraoperative margin assessment is essential to achieve a…
Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging
David Helminiak, Hang Hu, Julia Laskin +1
Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions. Given that most pixels within a sample's fiel…