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DTSTART;TZID=America/New_York:20201012T130000
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SUMMARY:ECE PhD Proposal Review: Muhamed Yildiz
DESCRIPTION:PhD Proposal Review: Interpretable Machine Learning for Retinopathy of Prematurity \nMuhamed Yildiz \nLocation: Zoom Link \nAbstract: Retinopathy of Prematurity (ROP)\, a leading cause of childhood blindness\, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease\, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels\, is the most important feature to determine treatment-requiring ROP. State-of-the-art ROP detection systems employ convolutional neural networks (CNNs) and achieve up to $0.947$ and $0.982$ area under the ROC curve (AUC) in the discrimination of and levels of ROP. However\, due to their black-box nature\, clinicians are reluctant to trust diagnostic predictions of CNNs. \nFirst\, we aim to create an interpretable\, feature extraction-based pipeline\, namely\, I-ROP ASSIST\, that achieves CNN like performance when diagnosing plus disease from retinal images. Our method segments retinal vessels\, detects the vessel centerlines. Then\, our method extracts features relevant to ROP\, including tortuosity and dilation measures\, and uses these features for classification via logistic regression\, support vector machines and neural networks to assess a severity score for the input. For predicting and levels of ROP on a dataset containing 5512 posterior retinal images\, we achieve $0.88$ and $0.94$ AUC\, respectively. Our system combining automatic retinal vessel segmentation\, tracing\, feature extraction and classification is able to diagnose plus disease in ROP with CNN like performance. \nFurthermore\, we aim to address the interpretability problem of CNN-based ROP detection system. Incorporating visual attention capabilities in CNNs enhances interpretability by highlighting regions in the images that CNNs utilize for prediction. Generic visual attention methods do not leverage structural domain information such as tortuosity and dilation of retinal blood vessels in ROP diagnosis. We propose the Structural Visual Guidance Attention Networks (SVGA-Net) method\, that leverages structural domain information to guide visual attention in CNNs. SVGA-Net achieves $0.979$ and $0.987$ AUC to predict and levels of ROP. Moreover\, SVGA-Net consistently results in higher AUC compared to visual attention CNNs without guidance\, baseline CNNs\, and CNNs with structured masks.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-muhamed-yildiz/
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DTSTART;TZID=America/New_York:20201012T140000
DTEND;TZID=America/New_York:20201012T150000
DTSTAMP:20260505T224622
CREATED:20201006T232350Z
LAST-MODIFIED:20201006T232350Z
UID:4412-1602511200-1602514800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sara Banian
DESCRIPTION:PhD Proposal Review: Content-Aware Design Assistance Frameworks for Graphic Design Layouts \nSara Banian \nLocation: Zoom Link \nAbstract: Layout is an important visual communication factor in graphic design that encompasses a page’s overall composition. During the different design stages\, designers express their requirements through images describing the interface’s visual layout\, hierarchical structure\, and content. They create wireframe layouts to meet user requirements and find relative design examples to gain inspiration and explore design alternatives. This is not only an iterative process\, but also a time-consuming one. \nIn this proposal\, we aim to design and evaluate design assistance methodologies to augment the process of layout design with a particular focus on visual search and wireframe creation in the context of mobile User Interface (UI) deign. For visual search\, we investigate how to find design examples that are relative to the design requirements of a UI layout. Layout retrieval is different from pixel-level image retrieval\, as it requires processing both the spatial layout and the content of the data to retrieve similar images. To achieve this\, I explore the problem of user interface image retrieval from both the data and the model side\, by collecting a more highly annotated\, well-suited dataset and proposing an object-detection based image retrieval model. The model takes as input a user interface image and retrieves the visually similar design examples. It uses object detection to identify the user interface components\, performs semantic segmentation to produce a hierarchical structure\, and trains an attention-aware multi-modal embedding network that leans the structure and content of the given layout design for relevant image retrieval. Results show that the system is capable of retrieving relative design examples through content analysis. Next\, I propose a generative framework to investigate how to generate layout wireframes according to user specifications and following common design practices and conventions. The generative framework aims at modeling the content of the UI layouts taking into account different layout variations and design features.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sara-banian/
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