Sharon Alpert

A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography

This paper addresses the problem of detection and classification of tumors in breast mammograms. We introduce a novel system that integrates several modules including a breast segmentation module and a fibroglandular tissue segmentation module into a modified cascaded region-based convolutional network. The method is evaluated on a large multi-center clinical dataset and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in healthcare continues to accelerate generalizing such an approach may have a profound impact on patient care in many applications.

Using image analytics to monitor retail store shelves

Using image analytics to monitor the contents and status of retail store shelves is an emerging trend with increasing business importance. Detecting and identifying multiple objects on store shelves involves a number of technical challenges. The particular nature of product package design, the arrangement of products on shelves, and the requirement to operate in unconstrained environments are just a few of the issues that must be addressed. We explain how we addressed these challenges in a system for monitoring planogram compliance, developed as part of a project with Tesco, a large multinational retailer. The new system offers store personnel an instant view of shelf status and a list of action items for restocking shelves. The core of the system is based on its ability to achieve high rates of product recognition, despite the very small visual differences between some products. This paper covers how state-of-the-art methods for object detection behave when applied to this problem. We also describe the innovative aspects of our implementation for size-scale-invariant product recognition and fine-grained classification.

A Cross Saliency Approach to Asymmetry-Based Tumor Detection

Identifying regions that break the symmetry within an organ or between paired organs is widely used to detect tumors on various modalities. Algorithms for detecting these regions often align the images and compare the corresponding regions using some set of features. This makes them prone to misalignment errors and inaccuracies in the estimation of the symmetry axis. Moreover, they are susceptible to errors induced by both inter and intra image correlations. We use recent advances in image saliency and extend them to handle pairs of images, by introducing an algorithm for image cross-saliency. Our cross-saliency approach is able to estimate regions that differ both in organ and paired organs symmetry without using image alignment, and can handle large errors in symmetry axis estimation. Since our approach is based on internal patch distribution it returns only statistically informative regions and can be applied as is to various modalities. We demonstrate the application of our approach on brain MRI and breast mammogram.

Hybrid Mass Detection in Breast MRI Combining Unsupervised Saliency Analysis and Deep Learning

To interpret a breast MRI study, a radiologist has to examine over 1000 images, and integrate spatial and temporal information from multiple sequences. The automated detection and classification of suspicious lesions can help reduce the workload and improve accuracy. We describe a hybrid mass-detection algorithm that combines unsupervised candidate detection with deep learning-based classification. The detection algorithm first identifies image-salient regions, as well as regions that are cross-salient with respect to the contralateral breast image. We then use a convolutional neural network (CNN) to classify the detected candidates into true-positive and false-positive masses. The network uses a novel multi-channel image representation; this representation encompasses information from the anatomical and kinetic image features, as well as saliency maps. We evaluated our algorithm on a dataset of MRI studies from 171 patients, with 1957 annotated slices of malignant (59%) and benign (41%) masses. Unsupervised saliency-based detection provided a sensitivity of 0.96 with 9.7 false-positive detections per slice. Combined with CNN classification, the number of false positive detections dropped to 0.7 per slice, with 0.85 sensitivity. The multi-channel representation achieved higher classification performance compared to single-channel images. The combination of domain-specific unsupervised methods and general-purpose supervised learning offers advantages for medical imaging applications, and may improve the ability of automated algorithms to assist radiologists.

Unsupervised detection of abnormalities in medical images using salient features

In this paper we propose a new method for abnormality detection in medical images which is based on the notion of medical saliency. The proposed method is general and is suitable for a variety of tasks related to detection of: 1) lesions and microcalcifications (MCC) in mammographic images, 2) stenoses in angiographic images, 3) lesions found in magnetic resonance (MRI) images of brain. The main idea of our approach is that abnormalities manifest as rare events, that is, as salient areas compared to normal tissues. We define the notion of medical saliency by combining local patch information from the lightness channel with geometric shape local descriptors. We demonstrate the efficacy of the proposed method by applying it to various modalities, and to various abnormality detection problems. Promising results are demonstrated for detection of MCC and of masses in mammographic images, detection of stenoses in angiography images, and detection of lesions in brain MRI. We also demonstrate how the proposed automatic abnormality detection method can be combined with a system that performs supervised classification of mammogram images into benign or malignant/premalignant MCCs. We use a well known DDSM mammogram database for the experiment on MCC classification, and obtain 80% accuracy in classifying images containing premalignant MCC versus benign ones. In contrast to supervised detection methods, the proposed approach does not rely on ground truth markings, and, as such, is very attractive and applicable for big corpus image data processing.

A CNN based method for automatic mass detection and classification in mammograms

Abstract A novel system for detection and classification of masses in breast mammography is introduced. The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score. While most of the previous work on mass identification in breast mammography has focused on classification, this study proposes to solve both the detection and the classification problems. The method is evaluated on a large multi-centre clinical data-set and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in health care continues to accelerate generalising such an approach may have a profound impact on patient care in many applications.