Abstract
Medical image segmentation is the task of objective segmentation in medical field. 3D Tumor segmentation can help physicians efficiently diagnose cancer, track tumor change, and make treatment plans. With the development of machine learning (ML)/Deep Learning (DL) image segmentation methods, the performance of medical image segmentation has significantly improved especially in terms of accuracy and time efficiency. Performance of typical deep learning algorithms such as Fully Connection Networks, Unet, DeepLab varies with respect to different datasets, pre-processing and training parameter settings. In this paper, we propose a new architecture which utilizes the advantages of various models and aggregates their results. The original concept was inspired by Ensembles of Multiple Models and Architectures. In this paper, we train different sub-models separately. Then we train a gating network to credit the inference result from each individual model to get a better result.
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Acknowledgment
This work was performed under the Grant 2015254 and gift by the National Science Foundation and Konica Minolta, respectively. We also acknowledge support from the University of Texas at Anderson Cancer Center, Texas Advanced Computing Center, and Oden Institute for Computational and Engineering Sciences initiative in Oncological Data and Computational Science.
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Shi, Y., Micklisch, C., Mushtaq, E., Avestimehr, S., Yan, Y., Zhang, X. (2022). An Ensemble Approach to Automatic Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_13
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