A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours.

Xiangwen Kong,Tao Hua,Guangming Zhu,Mohammed Bennamoun,Peiyi Shen,Xiaoyuan Lu,Ping Li,Syed Afaq Ali Shah,Johann Li

doi:10.3389/fdgth.2020.609349

Abstract

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.

Highlights

Lung cancer is caused by tumors which leads to the fastest increase in morbidity and mortality
In order to improve the performance of the basic model, we have used two attention mechanisms to enhance the feature before feeding it to the classifiers
This paper presents a dataset of lung lesions with fine contour annotation and attribute and explores the correlation between the attributes of the dataset

Summary

A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours

Ping Li 1†, Xiangwen Kong 2†, Johann Li 2†, Guangming Zhu 2*, Xiaoyuan Lu 1, Peiyi Shen 1, Syed Afaq Ali Shah 3, Mohammed Bennamoun 4 and Tao Hua 5. Reviewed by: Tao Chen, Virginia Tech, United States Jian Guo, RIKEN Center for Computational Science, Japan Zhimin Liu, Janssen Pharmaceuticals, Inc., United States. We introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. We define the structure of the dataset in detail, and discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. We use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. Experimental results show the relationship between different models and different levels of attributes

INTRODUCTION

Lung Nodule Datasets

Lung Nodule Classification

LUNG LESION DATASET

File Storage and Annotation Format

ATTRIBUTES AND PATHOLOGY

Attributes Description

Correlation Between Attributes

TASKS OF DATASET

Two Attention Mechanisms

EXPERIMENTAL RESULTS

Results for Input Modes