Title:Differential Diagnosis of Benign and Malignant Pulmonary Nodules in CT
Images Based on Multitask Learning
Volume: 20
Author(s): Guanghui Song*, Qi Dai, Yan Nie and Genlang Chen
Affiliation:
- School of Computer and Data Engineering, Ningbo Tech University, Ningbo 315100, Zhejiang, China
Keywords:
Multitask learning, Weakly supervised learning, Nodules detection, Semantic segmentation, Benign and malignant classification, Artificial intelligence.
Abstract:
Background:
Artificial intelligence-based aided diagnostic systems for pulmonary nodules can be divided into subtasks such as nodule detection, segmentation,
and benign and malignant differentiation. Most current studies are limited to single-target tasks. However, aided diagnosis aims to distinguish
benign from malignant pulmonary nodules, which requires the fusion of multiple-scale features and comprehensive discrimination based on the
results of multiple learning tasks.
Objective:
This study focuses on the aspects of model design, network structure, and constraints and proposes a novel model that integrates the learning tasks
of pulmonary nodule detection, segmentation, and classification under weakly supervised conditions.
Methods:
The main innovations include the following three aspects: (1) a two-dimensional sequence detection model based on a ConvLSTM (Convolutional
Long Short-Term Memory) network and U-shaped structure network is proposed to obtain the context space features of image slices fully; (2) a
differential diagnosis of benign and malignant pulmonary nodules based on multitask learning is proposed, which uses the annotated data of
different types of tasks to mine the potential common features among tasks; and (3) an optimization strategy incorporating prior knowledge of
computed tomography images and dynamic weight adjustment of multiple tasks is proposed to ensure that each task can efficiently complete
training and learning.
Results:
Experiments on the LIDC-IDRI and LUNA16 datasets showed that our proposed method achieved a final competition performance metric score of
87.80% for nodule detection and a Dice similarity coefficient score of 83.95% for pulmonary nodule segmentation.
Conclusion:
The cross-validation results of the LIDC-IDRI and LUNA16 datasets show that our model achieved 87.80% of the final competition performance
metric score for nodule detection and 83.95% of the DSC score for pulmonary nodule segmentation, representing the optimal result for that dataset.
