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Solitary Pulmonary Nodule

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Pulmonary nodule detection in CT scans with equivariant CNNs.

Medical image analysis
Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain for medical imaging problems. In this work we show that the sample complexity of CNNs can be significantly improved by usi...

Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.

Journal of healthcare engineering
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulm...

Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.

PloS one
A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extractio...

Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification.

Physics in medicine and biology
Existing deep-learning-based pulmonary nodule classification models usually use images and benign-malignant labels as inputs for training. Image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convo...

3-D Convolutional Neural Networks for Automatic Detection of Pulmonary Nodules in Chest CT.

IEEE journal of biomedical and health informatics
Deep two-dimensional (2-D) convolutional neural networks (CNNs) have been remarkably successful in producing record-breaking results in a variety of computer vision tasks. It is possible to extend CNNs to three dimensions using 3-D kernels to make th...

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.

IEEE journal of biomedical and health informatics
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous mod...

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate...

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT.

IEEE transactions on medical imaging
The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vis...