AIMC Topic: Lung Neoplasms

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The utilisation of convolutional neural networks in detecting pulmonary nodules: a review.

The British journal of radiology
Lung cancer is one of the leading causes of cancer-related fatality in the world. Patients display few or even no signs or symptoms in the early stages, resulting in up to 75% of patients diagnosed in the later stages of the disease. Consequently, th...

Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer.

Computers in biology and medicine
Multi-class classification has attracted much attention in cancer diagnosis and treatment and many machine learning methods have emerged for addressing this issue recently. However, class imbalance and gene selection problems occur in classifying lun...

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

Medical & biological engineering & computing
Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for ea...

Profiling Lung Cancer Patients Using Electronic Health Records.

Journal of medical systems
If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its ...

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new appro...

Small lung nodules detection based on local variance analysis and probabilistic neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination...

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

PloS one
We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vect...

Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.

IEEE journal of biomedical and health informatics
Compact binary representations of histopa-thology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality ...

Spatial extreme learning machines: An application on prediction of disease counts.

Statistical methods in medical research
Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing ...