Breast cancer is the most common cancer and one of the main causes of death in women. Early diagnosis of breast cancer is essential to ensure a high chance of survival for the affected women. Computer-aided detection (CAD) systems based on convolutional neural networks (CNN) could assist in the classification of abnormalities such as masses and calcifications. In this paper, several convolutional network models for the automatic classification of pathology in mammograms are analyzed. As well as different preprocessing and tuning techniques, such as data augmentation, hyperparameter tuning, and fine-tuning are used to train the models. Finally, these models are validated on various publicly available benchmark datasets.
Breast cancer in women is a worldwide health problem that has a high mortality rate. A strategy to reduce breast cancer mortality in women is to implement preventive programs such as mammography screening for early breast cancer diagnosis. In this presentation, a method for automatic detection of breast pathologies using a deep convolutional neural network and a class activation map is proposed. The neural network is pretrained on the regions of interest in order to modify the output layers to have two output classes. The proposed method is compared with different CNN models and applied to classify the public dataset Curated Breast Imaging Subset of DDSM (CBIS-DDSM).
In visual simultaneous localization and mapping (SLAM), the odometry estimation and navigation map building are carried out concurrently using only cameras. An important step in the SLAM process is the detection and analysis of the keypoints found in the environment. Performing a good correspondence of these points allows us to build an optimal point cloud for maximum localization accuracy of the mobile robot and, therefore, to build a precise map of the environment. In this presentation, we perform an extensive comparison study of the correspondences made by various combinations of detectors/descriptors and contrast the performance of two iterative closest points (ICP) algorithms used in the RGB-D SLAM problem. An adaptive RGB-D SLAM system is proposed, and its performance with the TUM RGB-D dataset is presented and discussed.
Visual SLAM is widely known in robotics for computing, concurrently, the odometry of a robot and construct a 3D navigation map with only a camera. In visual SLAM systems, detection and description of local features are extremely important because they identify unique and invariant points in an observed frame. Although there are various detectors and descriptors, the proper detector/descriptor combination for extraction has not yet been generalized for the problem. In this work, a comprehensive performance evaluation of combinations for different feature detectors and descriptors is presented. This evaluation will help determine the best detector/descriptor combination for designing a visual SLAM system based on RGB-D data. The considered methods are evaluated in terms of accuracy and robustness in both, a single and overall visual SLAM system.
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