Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation.

Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement.

Comparison of deep learning models for digital H&E staining from unpaired label-free multispectral microscopy images.

Background And Objective: This paper presents the quantitative comparison of three generative models of digital staining, also known as virtual staining, in H&E modality (i.e., Hematoxylin and Eosin) that are applied to 5 types of breast tissue.

Diffeomorphic transforms for data augmentation of highly variable shape and texture objects.

Background And Objective: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts can correctly categorize each sample. Experts are able to identify small changes in shape and texture which go unnoticed by untrained people, as well as distinguish between objects in the same class that present drastically different shapes and textures.

Automatic Museum Audio Guide.

An automatic "museum audio guide" is presented as a new type of audio guide for museums. The device consists of a headset equipped with a camera that captures exhibit pictures and the eyes of things computer vision device (EoT). The EoT board is capable of recognizing artworks using features from accelerated segment test (FAST) keypoints and a random forest classifier, and is able to be used for an entire day without the need to recharge the batteries.

Eyes of Things.

Embedded systems control and monitor a great deal of our reality. While some "classic" features are intrinsically necessary, such as low power consumption, rugged operating ranges, fast response and low cost, these systems have evolved in the last few years to emphasize connectivity functions, thus contributing to the Internet of Things paradigm. A myriad of sensing/computing devices are being attached to everyday objects, each able to send and receive data and to act as a unique node in the Internet.

Sample Selection for Training Cascade Detectors.

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set.

An automated system for whole microscopic image acquisition and analysis.

The field of anatomic pathology has experienced major changes over the last decade. Virtual microscopy (VM) systems have allowed experts in pathology and other biomedical areas to work in a safer and more collaborative way. VMs are automated systems capable of digitizing microscopic samples that were traditionally examined one by one.

Breast density classification to reduce false positives in CADe systems.

This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved.

CADe system integrated within the electronic health record.

The latest technological advances and information support systems for clinics and hospitals produce a wide range of possibilities in the storage and retrieval of an ever-growing amount of clinical information as well as in detection and diagnosis. In this work, an Electronic Health Record (EHR) combined with a Computer Aided Detection (CADe) system for breast cancer diagnosis has been implemented. Our objective is to provide to radiologists a comprehensive working environment that facilitates the integration, the image visualization, and the use of aided tools within the EHR.

Emerging trends: grid technology in pathology.

Grid technology has enabled clustering and access to, and interaction among, a wide variety of geographically distributed resources such as supercomputers, storage systems, data sources, instruments as well as special devices and services, realizing network-centric operations. Their main applications include large scale computational and data intensive problems in science and engineering. Grids are likely to have a deep impact on health related applications.