activity
20142023
most citedResolution-enhanced parallel coded ptychography for high-throughput optical imaging

92 citations · 120 across the 9 of their papers we have counts for

collaborators

9 papers

q-bio.GN202314 cited

DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks

Daoan Zhang, Weitong Zhang, Yu Zhao +4

Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge. To…

cs.LG2023

A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification

Mingcai Chen, Yu Zhao, Zhonghuang Wang +2

Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions…

cs.LG2023

Reweighted Mixup for Subpopulation Shift

Zongbo Han, Zhipeng Liang, Fan Yang +8

Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different…

cs.CV20223 cited

Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification

Zhikang Wang, Yue Bi, Tong Pan +6

Multiple instance learning (MIL) is a powerful approach to classify whole slide images (WSIs) for diagnostic pathology. A fundamental challenge of MIL on WSI classification is to d…

cs.CV20221 cited

ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images

Jiawei Yang, Hanbo Chen, Yuan Liang +3

Detectingandsegmentingobjectswithinwholeslideimagesis essential in computational pathology workflow. Self-supervised learning (SSL) is appealing to such annotation-heavy tasks. Des…

cs.CV2022

ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification

Jiawei Yang, Hanbo Chen, Yu Zhao +4

Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance learning (MIL) methods to handle gigapixel resolution images and slide-level labels.…