Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected lesions.

Purpose: Medical imaging-based machine learning (ML) for computer-aided diagnosis of lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.

Approach: Three feature extraction methods were investigated.

MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification.

Distinguishing malignant from benign lesions has significant clinical impacts on both early detection and optimal management of those early detections. Convolutional neural network (CNN) has shown great potential in medical imaging applications due to its powerful feature learning capability. However, it is very challenging to obtain pathological ground truth, addition to collected in vivo medical images, to construct objective training labels for feature learning, leading to the difficulty of performing lesion diagnosis.

Texture-aware dual domain mapping model for low-dose CT reconstruction.

Background: Remarkable progress has been made for low-dose computed tomography (CT) reconstruction tasks by applying deep learning techniques. However, establishing an intrinsic link between deep learning techniques and CT texture preservation is still one of the significant challenges for researchers to further improve the effect of low-dose CT (LDCT) reconstruction.

Purpose: Most of the existing deep learning-based LDCT reconstruction methods are derived from popular frameworks, and most models focus on the image domain.

Fully-automatic segmentation of coronary artery using growing algorithm.

Currently, cardiac computed tomography angiography (CTA) is widely applied to coronary artery disease diagnosis. Automatic segmentation of coronary artery has played an important role in coronary artery disease diagnosis. In this study, we propose and test a fully automatic coronary artery segmentation method that does not require any human-computer interaction.

A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only.

An adaptive paradigm for computer-aided detection of colonic polyps.

Most previous efforts in developing computer-aided detection (CADe) of colonic polyps apply similar measures or parameters to detect polyps regardless of their locations under an implicit assumption that all the polyps reside in a similar local environment, e.g. on a relatively flat colon wall.