63 citations · 146 across the 12 of their papers we have counts for
12 papers
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao +1
Contrastive Language-Image Pre-training (CLIP) models have shown promising performance on zero-shot visual recognition tasks by learning visual representations under natural langua…
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…
Anomaly Detection via Reverse Distillation from One-Class Embedding
Hanqiu Deng, Xingyu Li
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD).The representation discrepancy of anomalies in the teacher-…
A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer
Xingyu Li, Jitendra Jonnagaddala, Shuhua Yang +2
Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However,…
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Kaiyuan Liu, Xingyu Li, Yurui Lai +6
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones. Inspire…
CoverTheFace: face covering monitoring and demonstrating using deep learning and statistical shape analysis
Yixin Hu, Xingyu Li
Wearing a mask is a strong protection against the COVID-19 pandemic, even though the vaccine has been successfully developed and is widely available. However, many people wear them…
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…
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder
Xingyu Li, Marko Radulovic, Ksenija Kanjer +1
Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue prolifera…