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Multitask Cascade Convolution Neural Networks for Automatic Thyroid Nodule Detection and Recognition
- Source :
- IEEE journal of biomedical and health informatics. 23(3)
- Publication Year :
- 2018
-
Abstract
- Thyroid ultrasonography is a widely used clinical technique for nodule diagnosis in thyroid regions. However, it remains difficult to detect and recognize the nodules due to low contrast, high noise, and diverse appearance of nodules. In today's clinical practice, senior doctors could pinpoint nodules by analyzing global context features, local geometry structure, and intensity changes, which would require rich clinical experience accumulated from hundreds and thousands of nodule case studies. To alleviate doctors' tremendous labor in the diagnosis procedure, we advocate a machine learning approach to the detection and recognition tasks in this paper. In particular, we develop a multitask cascade convolution neural network (MC-CNN) framework to exploit the context information of thyroid nodules. It may be noted that our framework is built upon a large number of clinically confirmed thyroid ultrasound images with accurate and detailed ground truth labels. Other key advantages of our framework result from a multitask cascade architecture, two stages of carefully designed deep convolution networks in order to detect and recognize thyroid nodules in a pyramidal fashion, and capturing various intrinsic features in a global-to-local way. Within our framework, the potential regions of interest after initial detection are further fed to the spatial pyramid augmented CNNs to embed multiscale discriminative information for fine-grained thyroid recognition. Experimental results on 4309 clinical ultrasound images have indicated that our MC-CNN is accurate and effective for both thyroid nodules detection and recognition. For the correct diagnosis rate of malignant and benign thyroid nodules, its mean Average Precision (mAP) performance can achieve up to [Formula: see text] accuracy, which outperforms the common CNNs by [Formula: see text] on average. In addition, we conduct rigorous user studies to confirm that our MC-CNN outperforms experienced doctors, yet only consuming roughly [Formula: see text] ( 1/48) of doctors' examination time on average. Therefore, the accuracy and efficiency of our new method exhibit its great potential in clinical applications.
- Subjects :
- Thyroid nodules
Computer science
Feature extraction
Thyroid Gland
Health Informatics
Context (language use)
02 engineering and technology
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Health Information Management
Discriminative model
Image Interpretation, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Pyramid (image processing)
Thyroid Nodule
Electrical and Electronic Engineering
Ultrasonography
Artificial neural network
business.industry
Nodule (medicine)
Pattern recognition
medicine.disease
Computer Science Applications
020201 artificial intelligence & image processing
Artificial intelligence
Neural Networks, Computer
medicine.symptom
business
Algorithms
Subjects
Details
- ISSN :
- 21682208
- Volume :
- 23
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- IEEE journal of biomedical and health informatics
- Accession number :
- edsair.doi.dedup.....223507253acb1b13ba4f48031c586081