Philippe Burlina



Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acute Lyme disease by patients and clinicians is crucial to early diagnosis and treatment. Our objective in this study was to develop deep learning approaches using deep convolutional neural networks for   ...more

We address the challenge of finding anomalies in ultrasound images via deep learning, specifically applying this to screening for myopathies and finding rare presentations of myopathic disease. Among myopathic diseases, this study focuses on the use case of myositis given the spectrum of muscle involvement seen in these inflammatory muscle diseases   ...more

Efficient exploration through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond th   ...more

This study investigates unsupervised novelty detection (ND) for screening of rare myopathies and specifically myositis. To support this study we developed from the ground up a novel and fully annotated dataset consisting of 3586 images taken of eighty nine individuals obtained under informed consent during 2016-2017. We developed and compared perfo   ...more

An artificial intelligence (AI) using a deep-learning approach can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases and has the potential to be used in other image-based medical diagnoses.   ...more

This work studies joint camera and robotic manipulator control for reaching tasks in complex environments with obstacles and occluders. We obviate the conventional challenges involved in complex perception, planning, and control modules and careful calibration for sensing and actuation and seek a solution leveraging deep reinforcement learning (DRL   ...more

This work investigates a hybrid method based on random forests and deep image features to combine non-visual side channel information with image data for classification. We apply this to automated retinal image analysis (ARIA) and the detection of age-related macular degeneration (AMD). For evaluation, we use a dataset collected by the National In   ...more

Objective: To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Methods: Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included   ...more

Importance: Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the cho   ...more

Background: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing   ...more

We describe the recent development of assistive computer vision algorithms for use with the Argus II retinal prosthesis system. While users of the prosthetic system can learn and adapt to the limited stimulation resolution, there exists great potential for computer vision algorithms to augment the experience and significantly increase the utility   ...more

We examine hierarchical approaches to image classification problems that include categories for which we have no training examples. Building on prior work in hierarchical classification that optimizes the trade-off between depth in a tree and accuracy of placement, we compare the performance of multiple formulations of the problem on both previous   ...more

This work leverages Deep Reinforcement Learning (DRL) to make robotic control immune to changes in the robot manipulator or the environment and to perform reaching, collision avoidance and grasping without explicit, prior and fine knowledge of the human arm structure and kinematics, without careful hand-eye calibration, solely based on visual/reti   ...more

This study addresses the development of machine learning methods for reduced space ultrasound to perform automated prescreening of breast cancer. The use of ultrasound in low-resource settings is constrained by lack of trained personnel and equipment costs, and motivates the need for automated, low-cost diagnostic tools. We hypothesize a solution   ...more

Deep learning (DL) has led to near or better than human performance in image classification or object/speech recognition. DL is now providing new tools to address autonomous robotic manipulation and navigation challenges. One of the fundamental capabilities necessary for robotic manipulation is the ability to reorient objects within the hand. In th   ...more

Deep convolutional neural networks (DCNNs) perform on par or better than humans for image classification. Hence efforts have now shifted to more challenging tasks such as object detection and classification in images, video or RGBD. Recently developed region CNNs (R-CNN) such as Fast R-CNN [7] address this detection task for images. Instead, this   ...more

This study focuses on using ultrasound (US) biomarkers for characterizing myopathies and in particular myositis. US offers an opportunity to deliver diagnostics in clinical settings at a fraction of the cost and discomfort entailed in current workflows. US is also better suited for usage in under-resourced environments. This paper is focused on st   ...more

Our work is focused on the development of non-invasive methods to estimate skin constitutive elements. Such methods can play an important clinical and scientific role in detecting the early onset of skin tumors. Given current statistics by the American Academy of Dermatology suggesting that more than 10 people die each hour worldwide due to skin r   ...more

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