Abstract
Convolutional neural networks (CNNs) have recently been popular for classification and segmentation through numerous network architectures offering a substantial performance improvement. Their value has been particularly appreciated in the domain of biomedical applications, where even a small improvement in the predicted segmented region (e.g., a malignancy) compared to the ground truth can potentially lead to better diagnosis or treatment planning. Here, we introduce a novel architecture, namely the Overall Convolutional Network (O-Net), which takes advantage of different pooling levels and convolutional layers to extract more deeper local and containing global context. Our quantitative results on 2D images from two distinct datasets show that O-Net can achieve a higher dice coefficient when compared to either a U-Net or a Pyramid Scene Parsing Net. We also look into the stability of results for training and validation sets which can show the robustness of model compared with new datasets. In addition to comparison to the decoder, we use different encoders including simple, VGG Net, and ResNet. The ResNet encoder could help to improve the results in most of the cases.
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Acknowledgments
Research reported in this publication was partly supported by the National Institutes of Health (NIH) under award numbers NINDS:R01NS042645, NCI: R01CA161749, NCI:U24CA189523, NCI:U01CA242871. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH. This work was also supported by the Susan G. Komen for the Cure\(\circledR \) Breast Cancer Foundation [PDF17479714]. Also, we appreciate NVIDIA support for a donation of GPU to OHM.
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Maghsoudi, O.H., Gastounioti, A., Pantalone, L., Davatzikos, C., Bakas, S., Kontos, D. (2020). O-Net: An Overall Convolutional Network for Segmentation Tasks. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_21
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