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--- science_cases:iwf_science_case [2022/09/12 12:31] – admin
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 Starting from the reimplementation, a **post processing step based on YOLO v5** (ultralytics) was investigated, in order to improve performance. Even though first results seemed promising, the idea was later discarded due to unsatisfactory results and the laborious pipeline. Since the ultimate goal is an explicit and widely applicable pipeline, it was decided to abandon the general approach of using multiple basic [[:glossary#neural_network|neural networks ]]and the similarity measure used by Nguyen et al. (2019) completely and **compose it as a segmentation problem** instead.
-We proposed a pipeline using a **UNet ** (Ronneberger et al., 2015) including residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP) and attention blocks, similar to the ResUNet++ (Jha et al., 2019), for the automatic detection of ICMEs. Comparing it to our first results, we find that our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other data sets, as well as the three aligned data sets. The relatively fast training allows straightforward tuning of [[:glossary#hyperparameters|hyperparameters]]. Our proposed pipeline can be used for any time series segmentation problem. The straightforward implementation allows a simple extension to a [[:glossary#multi-class_classification|multi-class classification]] problem and paves the way to include corotating interaction regions into the range of detectable phenomena within our pipeline. Furthermore, we hope to apply our model to similar problems in the future.
+We proposed a pipeline using a **UNet ** (Ronneberger et al., 2015) including residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP) and attention blocks, similar to the **<nowiki>ResUNet++</nowiki>** (Jha et al., 2019), for the automatic detection of ICMEs. Comparing it to our first results, we find that our model outperforms the baseline regarding GPU usage, training time and robustness to missing [[:glossary#feature|features]], thus making it more usable for other data sets, as well as the three aligned data sets. The relatively fast training allows straightforward tuning of [[:glossary#hyperparameters|hyperparameters]]. Our proposed pipeline can be used for any time series segmentation problem. The straightforward implementation allows a simple extension to a [[:glossary#multi-class_classification|multi-class classification]] problem and paves the way to include corotating interaction regions into the range of detectable phenomena within our pipeline. Furthermore, we hope to apply our model to similar problems in the future.
-Results of this science case were presented at the EGU21, at EPSC2021, at ESWW 2021, and at AGU21. This ML pipeline was presented in a workshop at EPSC2021 and is, together with a tutorial, available on our GitHub repository. A publication was submitted to and accepted by the journal "Space Weather".
+Results of this science case were presented at the {{:wiki:esws2020-iwf_presentation.pdf|ESWS 2020}}, at {{:wiki:egu2021-ruedisser_etal.pdf|EGU21}}, at {{:wiki:esww2021-ruedisser_presentation.pdf|ESWW 2021}}, at {{:wiki:agu21_icme_ruedissser.pdf|AGU21}}, and at {{:wiki:mlhelio22_ruedisser_etal.pdf|ML-Helio 2022}}. This ML pipeline was presented in a [[https://github.com/epn-ml/EPSC2021-ICME-workshop|workshop at EPSC2021]] and is, together with a [[:tutorials_icme|tutorial]], available on our [[https://github.com/epn-ml/|GitHub repository]]. **A publication was submitted to and accepted by the journal "Space Weather".**
 **References: **
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- * Nguyen, G., et al. (2019), Automatic Detection of Interplanetary Coronal Mass Ejections from In Situ Data: A Deep Learning Approach, Astrophys. J. 874, 145, doi:10.3847/1538-4357/ab0d24
+* Nguyen, G., et al. (2019), Automatic Detection of Interplanetary Coronal Mass Ejections from In Situ Data: A Deep Learning Approach, Astrophys. J. 874, 145, doi:10.3847/1538-4357/ab0d24\\
-* Jha, D., et al. (2019), Resunet++: An advanced architecture for medical image segmentation, arXiv e-prints, arXiv:1911.07067
+* Jha, D., et al. (2019), Resunet++: An advanced architecture for medical image segmentation, arXiv e-prints, arXiv:1911.07067\\
 * Ronneberger, O., et al. (2015), U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. [[https://doi.org/10.1007/978-3-319-24574-4_28|https://doi.org/10.1007/978-3-319-24574-4_28]]
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