Life Sciences and Health Seminar Georgios Andreadis, Solon Pissis

Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images; Differentially Private String Sanitization for Frequency-Based Mining Tasks

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Meeting ID: 815 3524 9101
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Title:      Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images
Speaker:    Georgios Andreadis
Abstract:   Transferring information between images with large anatomical differences is an open challenge in medical image analysis. Existing methods require extensive up-front parameter tuning and have difficulty capturing large deformations and content mismatches. Using a multi-objective optimization approach with a dual-dynamic grid transformation model has previously proven effective at overcoming these issues while also producing a diverse set of high-quality registrations for 2D images. We successfully introduce the first method for multi-objective deformable image registration for 3D images, based on a new 3D dual-dynamic grid transformation model and using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) parallelized on a GPU. Our proof-of-concept prototype shows promising results on a synthetic and clinical registration problem. This talk is based on a conference paper to be published at the SPIE’22 Medical Imaging - Image Processing conference.

Title:      Differentially Private String Sanitization for Frequency-Based Mining Tasks
Speaker:    Solon P. Pissis
Abstract:   Strings are used to model genomic, natural language, and web activity data, and are thus often shared broadly. However, string data sharing has raised privacy concerns stemming from the fact that knowledge of length-k substrings of a string and their frequencies (multiplicities) may be sufficient to uniquely reconstruct the string; and from that the inference of such substrings may leak confidential information. We thus introduce the problem of protecting length-k substrings of a single string S by applying Differential Privacy (DP) while maximizing data utility for frequency-based mining tasks. Our theoretical and empirical evidence suggests that classic DP mechanisms are not suitable to address the problem. In response, we employ the order-k de Bruijn graph G of S and propose a sampling-based mechanism for enforcing DP on G. We consider the task of enforcing DP on G using our mechanism while preserving the normalized edge multiplicities in G. We define an optimization problem on integer edge weights that is central to this task and develop an algorithm based on dynamic programming to solve it exactly. We also consider two variants of this problem with real edge weights. By relaxing the constraint of integer edge weights, we are able to develop linear-time exact algorithms for these variants, which we use as stepping stones towards effective heuristics. An extensive experimental evaluation using real-world large-scale strings (in the order of billions of letters) shows that our heuristics are efficient and produce near-optimal solutions which preserve data utility for frequency-based mining tasks.

This talk is based on a conference paper presented at ICDM 2021 (joint work with Huiping Chen, Changyu Dong, Liyue Fan, Grigorios Loukides, and Leen Stougie).