activity
20112022
most citedDeepPap: Deep Convolutional Networks for Cervical Cell Classification

423 citations · 636 across the 11 of their papers we have counts for

collaborators

16 papers

eess.IV202211 cited

Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning

Jiawei Yang, Hanbo Chen, Jiangpeng Yan +2

Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and…

eess.IV20206 cited

Blind deblurring for microscopic pathology images using deep learning networks

Cheng Jiang, Jun Liao, Pei Dong +6

Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency.…

eess.IV20202 cited

Microscope Based HER2 Scoring System

Jun Zhang, Kuan Tian, Pei Dong +5

The overexpression of human epidermal growth factor receptor 2 (HER2) has been established as a therapeutic target in multiple types of cancers, such as breast and gastric cancers.…

eess.IV202057 cited

COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

Yifan Zhang, Shuaicheng Niu, Zhen Qiu +6

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer f…

cs.CV2019

Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data

Ling Zhang, Le Lu, Xiaosong Wang +4

Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment and surgical planning. Recent advances of co…

cs.CV2018

Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks

Haoming Lin, Yuyang Hu, Siping Chen +2

Fine-grained classification of cervical cells into different abnormality levels is of great clinical importance but remains very challenging. Contrary to traditional classification…