In this paper, we propose a novel deep learning architecture for joint classification and localization of abnormalities in mammograms. We first assume a weakly supervised setting and present a new approach with data driven decisions. This novel network combines two learning branches with region-level classification and region ranking. The network provides a global classification of the image into multiple classes, such as malignant, benign or normal. Our method further enables the localization of abnormalities as global class discriminative regions in full mammogram resolution. Next, we extend this method to a semi-supervised setting that engages a small set of local annotations, using a novel architecture, and a multi-task objective function. We present the impact of the local annotations on several performance measures, including localization, to evaluate the cost effectiveness of lesion annotation effort. Our evaluation is made over a large multi-center mammography dataset of @math 3,000 mammograms with various findings. Experimental results demonstrate the capabilities and advantages of the proposed method over previous weakly-supervised strategies, and the impact of semi-supervised learning. We show that targeting the annotation of only 5 of the images can significantly boost performance.
These methods deal with fusion of weak labels and a subset of data with local annotations, namely fully labeled (also known as strongly labeled) data. @cite_30 recently proposed a method for training Fast RCNN @cite_35 via Expectation-Maximization (EM). Focusing on the detection problem, they treated instance-level labels as missing data for weakly annotated images. Their method alternated between two steps: 1) E-step: estimating a probability distribution over all possible latent locations, and 2) M-step: updating a CNN using estimated locations from the last E-step. In the M-step, they optimize the sum of a region-level likelihood function from the fully supervised images and the estimation from the E-step. Their method was applied on non-medical (natural) images, and in practice, the quality of the final solution depended heavily on the initialization by another method ( @cite_24 , which we compare our method with). Furthermore, their approach required thousands of Fast RCNN training iterations at each M-step, that is computationally expensive, particularly for large images such as mammograms.
@cite_30 @cite_35 @cite_24
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Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an Expectation-Maximization (EM) based object detection method using deep convolutional neural networks (CNNs). Our method is applicable to both the weakly-supervised and semi-supervised settings. Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly supervised setting, our method provides significant detection performance improvement over current state-of-the-art methods, (2) having access to a small number of strongly (instance-level) annotated images, our method can almost match the performace of the fully supervised Fast RCNN. We share our source code at this https URL Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution. In this paper, we address this problem by exploiting the power of deep convolutional neural networks pre-trained on large-scale image-level classification tasks. We propose a weakly supervised deep detection architecture that modifies one such network to operate at the level of image regions, performing simultaneously region selection and classification. Trained as an image classifier, the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data. The model, which is a simple and elegant end-to-end architecture, outperforms standard data augmentation and fine-tuning techniques for the task of image-level classification as well.