Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived fro...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on medical imaging Ročník 36; číslo 3; s. 802 - 814
Hlavní autori:	Chen, Sihong, Qin, Jing, Ji, Xing, Lei, Baiying, Wang, Tianfu, Ni, Dong, Cheng, Jie-Zhi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.03.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Annotations Artificial neural networks Computation Computational modeling Computed tomography computed tomography (CT) Computer applications Computer-aided diagnosis (CAD) Consortia Deep learning Diagnostic systems feature learning Humans Lung Neoplasms - diagnostic imaging lung nodule Lung nodules Lungs Machine Learning Medical diagnosis Medical diagnostic imaging Medical imaging multi-task learning Neural networks Neural Networks (Computer) Nodules Noise reduction Radiographic Image Interpretation, Computer-Assisted - methods Ratings Search engines Semantics Solitary Pulmonary Nodule - diagnostic imaging Tomography, X-Ray Computed - methods
ISSN:	0278-0062, 1558-254X, 1558-254X
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0278-0062 1558-254X 1558-254X
DOI:	10.1109/TMI.2016.2629462