Michael Pekala

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks

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 choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. Objective To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Design, Setting, and Participants Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Main Outcomes and Measures Accuracy, receiver operating characteristics and area under the curve, and kappa score. Results The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-related Eye Disease Study data set. Conclusions and Relevance Applying a deep learning–based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.

An automated images-to-graphs framework for high resolution connectomics.

Reconstructing a map of neuronal connectivity is a critical challenge in contemporary neuroscience. Recent advances in high-throughput serial section electron microscopy (EM) have produced massive 3D image volumes of nanoscale brain tissue for the first time. The resolution of EM allows for individual neurons and their synaptic connections to be directly observed. Recovering neuronal networks by manually tracing each neuronal process at this scale is unmanageable, and therefore researchers are developing automated image processing modules. Thus far, state-of-the-art algorithms focus only on the solution to a particular task (e.g., neuron segmentation or synapse identification). In this manuscript we present the first fully-automated images-to-graphs pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue and produces a brain graph without any human interaction). To evaluate overall performance and select the best parameters and methods, we also develop a metric to assess the quality of the output graphs. We evaluate a set of algorithms and parameters, searching possible operating points to identify the best available brain graph for our assessment metric. Finally, we deploy a reference end-to-end version of the pipeline on a large, publicly available data set. This provides a baseline result and framework for community analysis and future algorithm development and testing. All code and data derivatives have been made publicly available in support of eventually unlocking new biofidelic computational primitives and understanding of neuropathologies.

Local distance metric learning for efficient conformal predictors

Conformal prediction is a relatively recent approach to classification that offers a theoretical framework for generating predictions with precise levels of confidence. For each new object encountered, a conformal predictor outputs a set of class labels that contains the true label with probability at least 1 - ∈, where ∈ is a user-specified error rate. The ability to predict with confidence can be extremely useful, but in many real-world applications unambiguous predictions consisting of a single class label are preferred. Hence it is desirable to design conformal predictors to maximize the rate of singleton predictions, termed the efficiency of the predictor. In this paper we derive a novel criterion for maximizing efficiency for a certain class of conformal predictors, show how concepts from local distance metric learning can provide a useful bound for maximizing this criterion, and demonstrate efficiency gains on real-world datasets.

Reconfiguring connected resource distribution systems

In this paper we consider complex mechanical systems that operate within harsh, uncertain environments where mission success depends critically upon the systems ability to monitor and maintain its own internal health. In particular, we focus on the problem of reconfiguring a systems internal resource distribution subsystems in response to one or more damage events. We present a graph-based approach that guarantees the resulting states adhere to a desirable resource segregation constraint. We also provide results using this approach in conjunction with optimization techniques to reconfigure a representative hardware test platform.

Online learning with minority class resampling

This paper considers using online binary classification for target detection where the goal is to identify signals of interest within a sequence of received signals generated by a shifting background. In this setting, we assume there is significant class imbalance (100∶1 or greater), the sequence of examples is arbitrarily long and the distribution of the majority (negative) class is slowly time-varying. This setting is typical in detection and classification problems in which time-varying effects are caused by some combination of shifting channel characteristics and interferers that enter and exit the scene. We show empirically that the addition of caching and minority class oversampling to online learners improves the g-means performance under these conditions by compensating for class imbalance.

Integrating Multi-Agent Systems: A Case Study

Intelligent Agent technology provides an appropriate framework to integrate knowledge with efficient production actions in distributed organizations. Integration of knowledge depends on balanced information representation within and across heterogeneous organizations. Integrating information within a specific environment can be helped by the deployment of standards and common practices. However, it is harder to attempt such a smooth integration with the information of foreign organizations. It is the challenge of this paper to present an architecture that provides a first step towards successful integration of separate multi-agent systems in a real life control domain.