Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis.

Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature representation is required. In this paper, we propose a new latent feature representation boosted by depth directional long-term recurrent learning for characterizing malignant masses.

Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection.

In digital breast tomosynthesis (DBT), image characteristics of projection views and reconstructed volume are different and both have the advantage of detecting breast masses, e.g. reconstructed volume mitigates a tissue overlap, while projection views have less reconstruction blur artifacts.

Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection.

In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masses from normal tissues on mammograms. In the proposed method, a given region of interest (ROI) for feature extraction is divided into three individual subregions, namely core, inner, and outer parts. The proposed region-based stellate features are then extracted to encode the different and complementary stellate pattern information by computing the statistical characteristics for each of the three different subregions.

Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes.

In digital breast tomosynthesis, the three dimensional (3D) reconstructed volumes only provide quasi-3D structure information with limited resolution along the depth direction due to insufficient sampling in depth direction and the limited angular range. The limitation could seriously hamper the conventional 3D image analysis techniques for detecting masses because the limited number of projection views causes blurring in the out-of-focus planes. In this paper, we propose a novel mass detection approach using slice conspicuity in the 3D reconstructed digital breast volumes to overcome the above limitation.

Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification.

We propose a novel computer-aided detection (CAD) framework of breast masses in mammography. To increase detection sensitivity for various types of mammographic masses, we propose the combined use of different detection algorithms. In particular, we develop a region-of-interest combination mechanism that integrates detection information gained from unsupervised and supervised detection algorithms.

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms.

Background: Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC).

Denoising 3D ultrasound volumes using sparse representation.

In this paper, a new 3D ultrasound (US) denoising technique that adopts the sparse representation has been proposed for an effective noise reduction in 3D US volumes. The purpose of the proposed method is to reduce image noise while preserving 3D objects edges, hence improving the human interpretation for clinical diagnosis and the 3D segmentation accuracy for further automatic malignancy detection. For denoising 3D US volumes, sparse representation was employed, which has showed an excellent performance in reducing Gaussian noise.

Combining multiple feature representations and AdaBoost ensemble learning for reducing false-positive detections in computer-aided detection of masses on mammograms.

One of the drawbacks of current Computer-aided Detection (CADe) systems is a high number of false-positive (FP) detections, especially for detecting mass abnormalities. In a typical CADe system, classifier design is one of the key steps for determining FP detection rates. This paper presents the effective classifier ensemble system for tackling FP reduction problem in CADe.