2024
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Liu C, Amodio M, Shen L, Gao F, Avesta A, Aneja S, Wang J, Del Priore L, Krishnaswamy S. CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. Lecture Notes In Computer Science 2024, 15008: 155-165. DOI: 10.1007/978-3-031-72111-3_15.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationLack of labeled dataUnsupervised deep learning frameworkSegmenting medical imagesDeep learning frameworkBrain MRI imagesRetinal fundus imagesContrastive learningLearning frameworkUnsupervised methodDeep learningExpert annotationsData topologyMedical imagesGranularity levelsEmbedding mapHausdorff distanceFundus imagesDice coefficientImage dataEmbeddingAnnotationLearningMRI images
2023
Wire Before You Walk
Asmara T, Bhaskar D, Adelstein I, Krishnaswamy S, Perlmutter M. Wire Before You Walk. 2023, 00: 714-716. DOI: 10.1109/ieeeconf59524.2023.10477089.Peer-Reviewed Original ResearchGraph Fourier MMD for Signals on Graphs
Leone S, Venkat A, Huguet G, Tong A, Wolf G, Krishnaswamy S. Graph Fourier MMD for Signals on Graphs. 2023, 00: 1-6. DOI: 10.1109/sampta59647.2023.10301384.Peer-Reviewed Original ResearchState-space characterizationEmbedding of distributionsRNA-sequencing data analysisSingle-cell RNA-sequencing data analysisMeaningful gene clustersPairs of distributionsOptimization problemProbability distributionGene clusterEuclidean spaceSpace characterizationAnalytical solutionSuch graphsGene embeddingsDisconnected graphsScale invarianceGene selectionGraphSuch distancesEmbeddingGenesNovel typeDistributionNumerous methodsBenchmark datasets
2020
Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings
Castro E, Benz A, Tong A, Wolf G, Krishnaswamy S. Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings. 2020, 00: 4519-4528. DOI: 10.1109/bigdata50022.2020.9378305.Peer-Reviewed Original Research
2019
Compressed Diffusion
Gigante S, Stanley J, Vu N, van Dijk D, Moon K, Wolf G, Krishnaswamy S. Compressed Diffusion. 2019, 00: 1-4. DOI: 10.1109/sampta45681.2019.9030994.Peer-Reviewed Original ResearchData regionsModern data analysisDiffusion mapsMost kernel methodsDiffusion geometryHeavy computational loadData pointsRelated embeddingsKernel-based methodsCubic complexityDiffusion map embeddingBig datasetsCorrelation kernelLower dimensionSpectral embeddingComputational loadKernel methodDiffusion relationManifold learningLocal geometryDiffusion processEmbeddingTheoretical connectionsGeometryIntrinsic structure