IMAL-Net: Interpretable multi-task attention learning network for invasive lung adenocarcinoma screening in CT images.
Med Phys
; 48(12): 7913-7929, 2021 Dec.
Article
in English
| MEDLINE | ID: covidwho-1516790
ABSTRACT
PURPOSE:
Feature maps created from deep convolutional neural networks (DCNNs) have been widely used for visual explanation of DCNN-based classification tasks. However, many clinical applications such as benign-malignant classification of lung nodules normally require quantitative and objective interpretability, rather than just visualization. In this paper, we propose a novel interpretable multi-task attention learning network named IMAL-Net for early invasive adenocarcinoma screening in chest computed tomography images, which takes advantage of segmentation prior to assist interpretable classification.METHODS:
Two sub-ResNets are firstly integrated together via a prior-attention mechanism for simultaneous nodule segmentation and invasiveness classification. Then, numerous radiomic features from the segmentation results are concatenated with high-level semantic features from the classification subnetwork by FC layers to achieve superior performance. Meanwhile, an end-to-end feature selection mechanism (named FSM) is designed to quantify crucial radiomic features greatly affecting the prediction of each sample, and thus it can provide clinically applicable interpretability to the prediction result.RESULTS:
Nodule samples from a total of 1626 patients were collected from two grade-A hospitals for large-scale verification. Five-fold cross validation demonstrated that the proposed IMAL-Net can achieve an AUC score of 93.8% ± 1.1% and a recall score of 93.8% ± 2.8% for identification of invasive lung adenocarcinoma.CONCLUSIONS:
It can be concluded that fusing semantic features and radiomic features can achieve obvious improvements in the invasiveness classification task. Moreover, by learning more fine-grained semantic features and highlighting the most important radiomics features, the proposed attention and FSM mechanisms not only can further improve the performance but also can be used for both visual explanations and objective analysis of the classification results.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Adenocarcinoma
/
Adenocarcinoma of Lung
/
Lung Neoplasms
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
Med Phys
Year:
2021
Document Type:
Article
Affiliation country:
Mp.15293
Similar
MEDLINE
...
LILACS
LIS