KEYWORDS: Digital mammography, Mammography, Architectural distortion, Data modeling, Breast cancer, Computer aided diagnosis and therapy, Breast, Computer-aided diagnosis, Digital imaging
This work presents a deep learning approach based on autoencoder to improve the detection of architectural distortion (AD) in digital mammography. AD can be the earliest sign of breast cancer, appearing before the formation of any mass or calcification. However, it is very diffcult to be detected and almost 50% of the cases are missed by the radiologists. Thus, we designed an autoencoder, based on a convolutional neural network (CNN), to work as a feature descriptor in a computer-aided detection (CAD) pipeline with the objective of detecting AD in digital mammography. This model was trained with 140,000 regions-of-interest (ROI) extracted from clinical mammograms. These samples were divided in two groups, with and without AD, according to the radiologist's report. Validation was done comparing the classifier performance when using the proposed autoencoder and other well-known feature descriptors, commonly used for the task of detecting AD in digital mammograms. The results showed that the performance of the autoencoder is slightly higher than that of other descriptors. However, the complexity and the computational cost of the autoencoder is much higher when compared to the hand-crafted descriptors.
This paper proposes a method to reduce the number of false-positives (FP) in a computer-aided detection (CAD) scheme for automated detection of architectural distortion (AD) in digital mammography. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automated detection of AD in breast images. The usual approach is automatically detect possible sites of AD in a mammographic image (segmentation step) and then use a classifier to eliminate the false-positives and identify the suspicious regions (classification step). This paper focus on the optimization of the segmentation step to reduce the number of FPs that is used as input to the classifier. The proposal is to use statistical measurements to score the segmented regions and then apply a threshold to select a small quantity of regions that should be submitted to the classification step, improving the detection performance of a CAD scheme. We evaluated 12 image features to score and select suspicious regions of 74 clinical Full-Field Digital Mammography (FFDM). All images in this dataset contained at least one region with AD previously marked by an expert radiologist. The results showed that the proposed method can reduce the false positives of the segmentation step of the CAD scheme from 43.4 false positives (FP) per image to 34.5 FP per image, without increasing the number of false negatives.
This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).
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