University of Central Florida Research Foundation Inc

The United States of America, As Represented by the Secretary, Department of Health and Human Services

Mayo Foundation for Medical Education and Research

Ulas Bagci

Enes Karaaslan, Jr

Fikret Necati Catbas

Naji Khosravan

Sarfaraz Hussein

Rodney LaLonde

Bradford Johns Wood

Michael B Wallace

Candice Bolan

Ismail Baris Turkbey

Rodney Duane LaLonde, Iii

Haydar Celik

A smart, human-centered technique that uses artificial intelligence and mixed reality to accelerate essential tasks of the inspectors such as defect measurement, condition assessment and data processing. For example, a bridge inspector can analyze some remote cracks located on a concrete pier, estimate their dimensional properties and perform condition assessment in real-time. The inspector can intervene in any step of the analysis/assessment and correct the operations of the artificial intelligence. Thereby, the inspector and the artificial intelligence will collaborate/communicate for improved visual inspection. This collective intelligence framework can be integrated in a mixed reality supported see-through headset or a hand-held device with the availability of sufficient hardware and sensors. Consequently, the methods reduce the inspection time and associated labor costs while ensuring reliable and objective infrastructure evaluation. Such methods offer contributions to infrastructure inspection, maintenance, management practice, and safety for the inspection personnel.

Methods of artificial intelligence-assisted infrastructure assessment using mixed reality systems

A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis.

Method for detection and diagnosis of lung and pancreatic cancers from imaging scans

An improved method of performing object segmentation and classification that reduces the memory required to perform these tasks, while increasing predictive accuracy. The improved method utilizes a capsule network with dynamic routing. Capsule networks allow for the preservation of information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for the reconstruction of an input image from output capsule vectors. The present invention expands the use of capsule networks to the task of object segmentation and medical image-based cancer diagnosis for the first time in the literature; extends the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules; extends the masked reconstruction to reconstruct the positive input class; and proposes a capsule-based pooling operation for diagnosis. The convolutional-deconvolutional capsule network shows strong results for the tasks of object segmentation and classification with substantial decrease in parameter space.

Deformable capsules for object detection

In accordance with some embodiments, systems, methods, and media for automatically diagnosing IPMNs using multi-modal MRI data are provided. In some embodiments, a system comprises: an MRI scanner; and a processor programmed to: prompt a user to select a slice of T1 and T2 MRI data including the subject's pancreas; generate minimum and maximum intensity projections based consecutive slices of the T1 and T2 MRI data; provide the projections to an image recognition CNN, and receive feature vectors for each from a fully connected layer; perform a canonical correlation analysis to determine correlations between the feature vectors; and provide a resultant vector to an SVM that determines whether the subject's pancreas includes IPMNs based on a vector.

Systems, methods, and media for automatically diagnosing intraductal papillary mucinous neosplasms using multi-modal magnetic resonance imaging data

Capsules for image analysis

A system and method for using gaze information to extract visual attention information combined with computer derived local saliency information from medical images to (1) infer object and background cues from a region of interest indicated by the eye-tracking and (2) perform a medical image segmentation process. Moreover, an embodiment is configured to notify a medical professional of overlooked regions on medical images and/or train the medical professional to review regions that he/she often overlooks.

Eye tracking applications in computer aided diagnosis and image processing in radiology

A system and method for automatically detecting and quantifying adiposity distribution is presented herein. The system detects, segments and quantifies white and brown fat adipose tissues at the whole-body, body region, and organ levels.

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Orlando, FL, United States of America

Ulas Bagci