In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors.

Early diagnosis of prostate cancer significantly improves a patient's 5-year survival rate. Biopsy of small prostate cancers is improved with image-guided biopsy. MRI-ultrasound fusion-guided biopsy is sensitive to smaller tumors but is underutilized due to the high cost of MRI and fusion equipment. Micro-ultrasound (micro-US), a novel high-resolution ultrasound technology, provides a cost-effective alternative to MRI while delivering comparable diagnostic accuracy. However, the interpretation of micro-US is challenging due to subtle gray scale changes indicating cancer vs normal tissue. This challenge can be addressed by training urologists with a large dataset of micro-US images containing the ground truth cancer outlines. Such a dataset can be mapped from surgical specimens (histopathology) onto micro-US images via image registration. In this paper, we present a semi-automated pipeline for registering in vivo micro-US images with ex vivo whole-mount histopathology images. Our pipeline begins with the reconstruction of pseudo-whole-mount histopathology images and a 3-dimensional (3D) micro-US volume. Each pseudo-whole-mount histopathology image is then registered with the corresponding axial micro-US slice using a two-stage approach that estimates an affine transformation followed by a deformable transformation. We evaluated our registration pipeline using micro-US and histopathology images from 18 patients who underwent radical prostatectomy. The results showed a Dice coefficient of 0.94 and a landmark error of 2.7 mm, indicating the accuracy of our registration pipeline. This proof-of-concept study demonstrates the feasibility of accurately aligning micro-US and histopathology images. To promote transparency and collaboration in research, we will make our code and dataset publicly available.

Histopathology tissue samples are widely available in two states: paraffin-embedded unstained and non-paraffin-embedded stained whole slide RGB images (WSRI). Hematoxylin and eosin stain (H&E) is one of the principal stains in histology but suffers from several shortcomings related to tissue preparation, staining protocols, slowness and human error. We report two novel approaches for training machine learning models for the computational H&E staining and destaining of prostate core biopsy RGB images. The staining model uses a conditional generative adversarial network that learns hierarchical non-linear mappings between whole slide RGB image (WSRI) pairs of prostate core biopsy before and after H&E staining. The trained staining model can then generate computationally H&E-stained prostate core WSRIs using previously unseen non-stained biopsy images as input. The destaining model, by learning mappings between an H&E stained WSRI and a non-stained WSRI of the same biopsy, can computationally destain previously unseen H&E-stained images. Structural and anatomical details of prostate tissue and colors, shapes, geometries, locations of nuclei, stroma, vessels, glands and other cellular components were generated by both models with structural similarity indices of 0.68 (staining) and 0.84 (destaining). The proposed staining and destaining models can engender computational H&E staining and destaining of WSRI biopsies without additional equipment and devices.

One hundred years from now, hospitals will be nearly unrecognizable as care moves to the outpatient setting and organizations integrate artificial intelligence, telemedicine and other IT applications to care for patients outside the walls of their institution. Forty-five healthcare executives, including five from hospital C-suites, describe the key trends disrupting the traditional hospital and how institutions can prepare for the future. Here is what 45 healthcare executives had to say about the hospital of the future. Responses are organized by category -- hospital CEOs and executives, physicians, health IT leaders, consultants and healthcare firms and organizations -- and in alphabetical order within each category. Responses have been edited lightly for length and clarity. Executive Vice President and Chief Strategy Officer at Memorial Hermann (Houston): "For decades, healthcare institutions operated under the assumption that people who are sick or injured should be seen by a ...

The idea of tiny, ubiquitous computers monitoring us and our environments from every nook and cranny might alarm a few civil libertarians – but this is exactly the concept driving researchers who are trying to perfect networks of smart, wireless sensors. They envision sensors sprinkled across a battlefield to warn of an enemy advance, or attached to pill bottles to alert caregivers to when an elderly patient takes (or doesn't take) his or her medication. They imagine faulty equipment in manufacturing plants that reports its own failures. In short, they see a pervasive grid of smart sensors that monitor, analyze, and network the bits and bytes of life. For years, these networks have remained largely in the prototype stage, not quite ready to hit the market. "We know that it is a good concept; we've demonstrated its feasibility," says Osman Ahmed, senior principal engineer at Siemens Building Technologies in Buffalo Grove, IL.