Search Results for author: Tieliang Gong

Found 13 papers, 2 papers with code

Robust and Fast Measure of Information via Low-rank Representation

1 code implementation30 Nov 2022 Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen, Chen Li

The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution.

Optimal Randomized Approximations for Matrix based Renyi's Entropy

no code implementations16 May 2022 Yuxin Dong, Tieliang Gong, Shujian Yu, Chen Li

The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks.

Density Estimation

Error-based Knockoffs Inference for Controlled Feature Selection

no code implementations9 Mar 2022 Xuebin Zhao, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong, Yulong Wang, Feng Zheng

Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings.

Feature Importance

Computationally Efficient Approximations for Matrix-based Renyi's Entropy

no code implementations27 Dec 2021 Tieliang Gong, Yuxin Dong, Shujian Yu, Bo Dong

The recently developed matrix based Renyi's entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution.

Markov subsampling based Huber Criterion

no code implementations12 Dec 2021 Tieliang Gong, Yuxin Dong, Hong Chen, Bo Dong, Chen Li

Subsampling is an important technique to tackle the computational challenges brought by big data.

Meta Mask Correction for Nuclei Segmentation in Histopathological Image

no code implementations24 Nov 2021 Jiangbo Shi, Chang Jia, Zeyu Gao, Tieliang Gong, Chunbao Wang, Chen Li

However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain.

Meta-Learning Nuclear Segmentation

PIMIP: An Open Source Platform for Pathology Information Management and Integration

no code implementations9 Nov 2021 Jialun Wu, Anyu Mao, Xinrui Bao, Haichuan Zhang, Zeyu Gao, Chunbao Wang, Tieliang Gong, Chen Li

However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured description of relevant patient information.

Management

A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

no code implementations26 Oct 2021 Jialun Wu, Haichuan Zhang, Zeyu Gao, Xinrui Bao, Tieliang Gong, Chunbao Wang, Chen Li

Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs).

Classification whole slide images

W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image

no code implementations26 Oct 2021 Anyu Mao, Jialun Wu, Xinrui Bao, Zeyu Gao, Tieliang Gong, Chen Li

In order to take advantage of segmentation methods based on point annotation, further alleviate the manual workload, and make cancer diagnosis more efficient and accurate, it is necessary to develop an automatic nucleus detection algorithm, which can automatically and efficiently locate the position of the nucleus in the pathological image and extract valuable information for pathologists.

A Personalized Diagnostic Generation Framework Based on Multi-source Heterogeneous Data

no code implementations26 Oct 2021 Jialun Wu, Zeyu Gao, Haichuan Zhang, Ruonan Zhang, Tieliang Gong, Chunbao Wang, Chen Li

In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient.

whole slide images

BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network

no code implementations26 Oct 2021 Jialun Wu, Yang Liu, Zeyu Gao, Tieliang Gong, Chunbao Wang, Chen Li

To address this issue, we propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports.

Knowledge Graphs Transfer Learning

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

no code implementations NeurIPS 2020 Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen

For high-dimensional observations in real environment, e. g., Coronal Mass Ejections (CMEs) data, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of prior knowledge on variable structure.

Additive models Bilevel Optimization +1

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