Unsupervised body part regression using convolutional neural network with self-organization (arXiv)
Ke Yan, Le Lu, and Ronald M. Summers

We propose unsupervised body part regressor (UBR), which builds a coordinate system for the body and outputs a continuous score for each slice. The score represents the normalized position of the body part in the slice.

UBR architecture

DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations (arXiv)
Ke Yan, Xiaosong Wang, Le Lu, and Ronald M. Summers

Lesion annotations are mined from PACS. A lesion detection algorithm based on Faster RCNN and an unsupervised categorization framework is proposed.

DeepLesion detection results


Learning classification and regression models for data with drift based on transfer samples (pdf, Matlab code)
Ke Yan, David Zhang, and Yong Xu IEEE Trans. on Instrumentation and Measurement (IF = 2.456), 2017.

We aim to compensate the two largest influential factors in e-nose applications: instrumental variation and time-varying drift. The transfer-sample-based multitask learning (TMTL) algorithm defines each device or time period as a domain, then learns a prediction model for each domain. Two paradigms, parallel and serial transfer, are designed according to the prior knowledge of the two influential factors. A dynamic model strategy is also proposed to handle continuous sample stream in the MTL framework.

Model similarity priors in transfer-sample-based multitask learning (TMTL)

Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization (pdf, Matlab code, domain adaptation toolbox)
Ke Yan, Lu Kou, and David Zhang
IEEE Trans. on Cybernetics (IF = 4.943), 2017.

Maximum independence domain adaptation (MIDA) is proposed to learn a domain-invariant subspace. It can be applied in all kinds of domain adaptation problems, including discrete or continuous distributional change, supervised/semi-supervised/unsupervised, multiple domains, classification or regression, etc. We first design "domain features", then maximize the independence between them and the learned features.

Effect of MIDA

Correcting instrumental variation and time-varying drift: a transfer learning approach with autoencoders (pdf, Python code)
Ke Yan and David Zhang
IEEE Trans. on Instrumentation and Measurement (IF = 1.790), 2016.

Drift correction autoencoder (DCAE) is proposed to correct instrumental variation and time-varying drift. We enhance the structure and objective function of the original autoencoder, so that the two factors can be explicitly modeled and compensated. A drift-corrected and discriminative representation of the original data is thus learned.

The structure of drift correction autoencoder (DCAE)

Calibration transfer and drift compensation of e-noses via coupled task learning (pdf, Matlab code)
Ke Yan and David Zhang
Sensors and Actuators B: Chemical (IF = 4.097), 2015.

The preliminary version of TMTL with only 2 tasks. The key idea is to align the transfer samples at the model level.

Improving the transfer ability of prediction models for electronic noses (pdf, Matlab code)
Ke Yan and David Zhang
Sensors and Actuators B: Chemical (IF = 4.097), 2015.

A novel variable standardization method and a regularization strategy, windowed piecewise direct standardization (WPDS) and standardization-error-based model improvement (SEMI), are proposed to deal with instrumental variation.

Feature selection and analysis on correlated gas sensor data with recursive feature elimination (pdf, Matlab code)
Ke Yan and David Zhang
Sensors and Actuators B: Chemical (IF = 4.097), 2015.

Introducing correlation bias reduction (CBR), an improvement to the feature selection algorithm SVM-RFE for correlated features.

Design of a breath analysis system for diabetes diagnosis and blood glucose level prediction (pdf)
Ke Yan, David Zhang, Darong Wu, Hua Wei, and Guangming Lu
IEEE Trans. on Biomedical Engineering (IF = 2.347), 2014.

The hardware and software of an e-nose-based system are introduced.

Breath analysis procedure

Blood glucose prediction by breath analysis system with feature selection and model fusion (pdf)
Ke Yan and David Zhang
36th Annual Intl. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, 2014, oral presentation

Predicting the blood glucose of 203 breath samples, mean relative absolute error = 20.69%.

Sensor evaluation in a breath analysis system (pdf)
Ke Yan and David Zhang
Intl. Conf. Medical Biometrics (ICMB), Shenzhen, 2014

A novel breath analysis system for diabetes diagnosis (pdf)
Ke Yan and David Zhang
Intl. Conf. Computerized Healthcare (ICMB), Hong Kong, 2012

Gabor Surface Feature for face recognition (pdf, Matlab code, OpenCV code)
Ke Yan, Youbin Chen and David Zhang
1st Asian Conference on Pattern Recognition (ACPR), Beijing, 2011

An improvement to the Gabor+LBP feature. Good performance on FERET dataset.

Patent: An exhaled gas detection system (in Chinese), Pending
一种呼出气体的检测系统 (link)
张大鹏, 闫轲, 卢光明
公开中, 公开号 CN103245705 A