ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

MobileRF: A Robust Device-Free Tracking System Based On a Hybrid Neural Network HMM Classifier.

We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a person's movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to perform tracking accurately. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors.

Measuring in-home walking speed using wall-mounted RF transceiver arrays.

In this paper we present a new method for passively measuring walking speed using a small array of radio transceivers positioned on the walls of a hallway within a home. As a person walks between a radio transmitter and a receiver, the received signal strength (RSS) detected by the receiver changes in a repeatable pattern that may be used to estimate walking speed without the need for the person to wear any monitoring device. The transceivers are arranged as an array of 4 with a known distance between the array elements. Walking past the first pair of transceivers will cause a peak followed by a second peak when the person passes the second pair of transceivers. The time difference between these peaks is used to estimate walking speed directly. We further show that it is possible to estimate the walking speed by correlating the shape of the signal using a single pair of transceivers positioned across from each other in a hallway or doorframe. RMSE performance was less than 15 cm/s using a 2-element array, and less than 8 cm/s using a 4-element array relative to a gait mat used for ground truth.

Multiharmonic tracking using sigma-point Kalman filter.

Several groups have proposed the state-space approach to track time-varying frequencies ofmulti-harmonic quasi-periodic signals contaminated with white Gaussian noise. We compared the extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF) algorithms on this problem. On average, the SPKF outperformed the EKF and more accurately tracked the instantaneous frequency over a wide range of signal-to-noise (SNR) ratios.

Noise reduction for heart sounds using a modified minimum-mean squared error estimator with ECG gating.

In this paper, we present a method for single channel noise reduction of heart sound recordings. Multiple noise sources, such as lung sounds, muscle contraction, and background noise can contaminate the heart sound collection making subsequent analysis difficult. Our approach is based on a spectral domain minimum-mean squared error (MMSE) estimation, originally introduced by Ephraim and Malah in the context of speech enhancement. This method uses a "decision-directed" approach to estimate the noise spectrum without the need for a separate reference signal. The noise spectrum is used to compute the SNR on-line for adapting the Wiener filter gain applied to the spectral amplitudes. A number of modifications are made to the baseline algorithm to increase the level of noise reduction while simultaneously reducing signal distortion. Enhancements include the use of a "soft" threshold to determine when to update the noise spectrum, a forward-backward filtering implementation (i.e., smoothing), and a "second-pass" iterative estimation scheme in which the residual noise is used to re-estimate the SNR and update the Wiener gains. In addition, ECG analysis is used to provide gating information on when desired heart sounds may be present in order to further guide the noise spectral estimation procedure. The noise reduction algorithm is tested as a front-end to an automatic heart sound analysis system. The sounds are collected through two sensors that act simultaneously as microphones and ECG electrodes. The proposed algorithm demonstrates improvements over existing noise reduction approaches in terms of SNR gain, qualitative evaluations, and automatic detection of abnormalities present in the heart sounds.