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A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos

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CycleTrack: A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos

Luojie Huang, Gregory N. McKay, Nicholas J. Durr*

Introduction

This is the official PyTorch implementation of CycleTrack (MICCAI 2021). For more technical details, please refer to: https://arxiv.org/abs/2011.13371

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Citation

Huang L., McKay G.N., Durr N.J. (2021) A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos. In: de Bruijne M. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_40

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A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos

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