63 citations · 146 across the 12 of their papers we have counts for
6 papers · 1 filter
BMAD: Benchmarks for Medical Anomaly Detection
Jinan Bao, Hanshi Sun, Hanqiu Deng +3
Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medi…
Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data
Yinsheng He, Xingyu Li
Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E)…
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model
Xingyu Li, Min Cen, Jinfeng Xu +2
Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology.Pre-trained neural networks based on ImageNet da…
Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images
Xingyu Li
Multiple stains are usually used to highlight biological substances in biomedical image analysis. To decompose multiple stains for co-localization quantification, blind source sepa…
Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning
Hanwen Liang, Konstantinos N. Plataniotis, Xingyu Li
Computational histopathology image diagnosis becomes increasingly popular and important, where images are segmented or classified for disease diagnosis by computers. While patholog…
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?
Xingyu Li, Konstantinos N. Plataniotis
Deep learning has achieved a great success in natural image classification. To overcome data-scarcity in computational pathology, recent studies exploit transfer learning to reuse…