Rutgers, The State University of New Jersey

University of Pennsylvania

Case Western Reserve University

University of Pa.

Michael D Feldman

Anant Madabhushi

John Tomaszewski

Mark Rosen

Andrew Janowczyk

Pallavi Tiwari

Satish E Viswanath

Jianbo Shi

James P Monaco

Ajay Basavanhally

Robert Toth

Kenneth B Margulies

Scott Doyle

Jeffrey John Nirschl

Jonathan Chappelow

Eliot G Peyster

John Tomaszeweski

Methods, apparatus, and other embodiments predict heart failure from WSIs of cardiac histopathology using a deep learning convolutional neural network (CNN). One example apparatus includes a pre-processing circuit configured to generate a pre-processed WSI by downsampling a digital WSI; an image acquisition circuit configured to randomly select a set of non-overlapping ROIs from the pre-processed WSI, and configured to provide the set of non-overlapping ROIs to a deep learning circuit; a deep learning circuit configured to generate an image-level probability that a member of the set of non-overlapping ROIs is a failure/abnormal pathology ROI using a CNN; and a classification circuit configured to generate a patient-level probability that the patient from which the region of tissue represented in the WSI was acquired is experiencing failure or non-failure based, at least in part, on the image-level probability.

Histomorphometric classifier to predict cardiac failure from whole-slide hematoxylin and eosin stained images

The present invention relates to a method and system for detecting biologically relevant structures in a hierarchical fashion, beginning at a low-resolution and proceeding to higher levels of resolution. The present invention also provides probabilistic pairwise Markov models (PPMMs) to classify these relevant structures. The invention is directed to a novel classification approach which weighs the importance of these structures. The present invention also provides a fast, efficient computer-aided detection/diagnosis (CAD) system capable of rapidly processing medical images (i.e. high throughput). The computer-aided detection/diagnosis (CAD) system of the present invention allows for rapid analysis of medical images the improving the ability to effectively detect, diagnose, and treat certain diseases.

System and method for accurate and rapid identification of diseased regions on biological images with applications to disease diagnosis and prognosis

This invention relates to medical image registration. Specifically, the invention relates to a combined feature ensemble mutual information (COFEMI) for robust inter-modal, inter-protocol image registration.

Combined feature ensemble mutual information image registration

This invention relates to computer-assisted diagnostics and classification of prostate cancer. Specifically, the invention relates to segmentation of the prostate boundary on MRI images, cancer detection using multimodal multi-protocol MR data; and their integration for a computer-aided diagnosis and classification system for prostate cancer.

Computer assisted diagnosis (CAD) of cancer using multi-functional, multi-modal in-vivo magnetic resonance spectroscopy (MRS) and imaging (MRI)

This invention relates to computer-aided diagnostics using content-based retrieval of histopathological image features. Specifically, the invention relates to the extraction of image features from a histopathological image based on predetermined criteria and their analysis for malignancy determination.

Malignancy diagnosis using content-based image retreival of tissue histopathology

This invention relates to supervised or unsupervised classification of biological datasets. Specifically, the invention relates to the use of Graph Embedding as a method of reducing dimensionality thereby improving supervised classification of classes, both conventional and new ones.

Systems and methods for classification of biological datasets

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Wilmington, DE, United States of America

Michael D Feldman