Raghav

Raghav

Assistant Professor @ Machine Learning Section, Kiehn Lab & Data Science Lab, University of Copenhagen

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Posts

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portfolio

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publications

Bayesian tracking of multiple point targets using expectation maximization
In Chalmers University (MSc Thesis), 2015. (link)
A batch algorithm for estimating trajectories of point targets using expectation maximization
In IEEE Transactions on Signal Processing, 2016. (link)
Extraction of airway trees using multiple hypothesis tracking and template matching
In the proceedings of The Sixth International Workshop on Pulmonary Image Analysis. MICCAI, 2016. (link)
Extraction of airways with probabilistic state-space models and Bayesian smoothing
In the proceedings of Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics, 2017. (link)
Extraction of Airways using Graph Neural Networks
In the proceedings of 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam., 2018. (link)
Extraction of Airways from Volumetric Data
In University of Copenhagen (PhD Thesis), 2018. (link)
Mean field network based graph refinement with application to airway tree extraction
In the proceedings of 21st Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2018), Grenada, Spain, 2018. (link)
A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs
In the proceedings of International Workshop on Machine Learning in Medical Imaging, 2019. (link)
Extracting tree-structures in CT data by tracking multiple statistically ranked hypotheses
In the International Journal of Medical Physics Research and Practice, 2019. (link)
Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies
In IEEE transactions on medical imaging, 2019. (link)
Segmentation of roots in soil with U-Net
In Journal of Plant Methods, 2019. (link)
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
In Medical Image Analysis, 2020. (link)
Locomotor deficits in ALS mice are paralleled by loss of V1-interneuron-connections onto fast motor neurons
In BioRxiv, 2020. (link)
Tensor Networks for Medical Image Classification
In the proceedings of International Conference on Medical Imaging with Deep Learning (MIDL). Winner of Runner-up Best Paper Award., 2020. (link)
Lung Segmentation from Chest X-rays using Variational Data Imputation
In ICML Workshop on The Art of Learning with Missing Values (Artemiss-2020), 2020. (link)
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
In ICML Workshop on Challenges in Deploying and monitoring Machine Learning Systems, 2020. (link)
Uncertainty quantification in medical image segmentation with Normalizing Flows
In 11th International Workshop on Machine Learning in Medical Imaging, 2020. (link)
Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models
In 16th International Workshop on Mining and Learning with Graphs, 2020. (link)
Locally orderless tensor networks for classifying two- and three-dimensional medical images
In Arxiv, 2020. (link)
Multi-layered tensor networks for image classification
In First Workshop on Quantum Tensor Networks in Machine Learning. In conjunction with 34th NeurIPS, 2020., 2020. (link)
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
In Scientific Reports, 2021. (link)
Dynamic β-VAEs for quantifying biodiversity by clustering optically recorded insect signals
In Arxiv, 2021. (link)
Segmenting two-dimensional structures with strided tensor networks
In 27th international conference on Information Processing in Medical Imaging (IPMI), 2021. (link)
Detection of foraging behavior from accelerometer data using U-Net type convolutional networks
In Ecological Informatics, 2021. (link)

talks

teaching