activity
20182022
most citedBenchmarking Domain Generalization on EEG-based Emotion Recognition

7 citations · 10 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2022

Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges

Enwei Zhu, Yiyang Liu, Ming Jin +1

Many recent named entity recognition (NER) studies criticize flat NER for its non-overlapping assumption, and switch to investigating nested NER. However, existing nested NER model…

eess.SP20227 cited

Benchmarking Domain Generalization on EEG-based Emotion Recognition

Yan Li, Hao Chen, Jake Zhao +2

Electroencephalography (EEG) based emotion recognition has demonstrated tremendous improvement in recent years. Specifically, numerous domain adaptation (DA) algorithms have been e…

cs.CV2021

MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions

Penghua Zhai, Huaiwei Cong, Gangming Zhao +4

\emph{Objective and Impact Statement}. With the renaissance of deep learning, automatic diagnostic systems for computed tomography (CT) have achieved many successful applications.…

cs.LG20213 cited

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition

Hao Chen, Ming Jin, Zhunan Li +3

As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress du…

cs.LG2021

Investigating Critical Risk Factors in Liver Cancer Prediction

Jinpeng Li, Yaling Tao, Ting Cai

We exploit liver cancer prediction model using machine learning algorithms based on epidemiological data of over 55 thousand peoples from 2014 to the present. The best performance…

cs.CV2021

Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning

Gangming Zhao, Baolian Qi, Jinpeng Li

Locating lesions is important in the computer-aided diagnosis of X-ray images. However, box-level annotation is time-consuming and laborious. How to locate lesions accurately with…