TY - GEN
T1 - O-Net
T2 - 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
AU - Maghsoudi, Omid Haji
AU - Gastounioti, Aimilia
AU - Pantalone, Lauren
AU - Davatzikos, Christos
AU - Bakas, Spyridon
AU - Kontos, Despina
N1 - Funding Information:
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 Breast Cancer Foundation [PDF17479714]. Also, we appreciate NVIDIA support for a donation of GPU to OHM.
Funding Information:
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? Breast Cancer Foundation [PDF17479714]. Also, we appreciate NVIDIA support for a donation of GPU to OHM.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Biomedical imaging
KW - Deep learning
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85092713915&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59861-7_21
DO - 10.1007/978-3-030-59861-7_21
M3 - Conference contribution
AN - SCOPUS:85092713915
SN - 9783030598600
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 199
EP - 209
BT - Machine Learning in Medical Imaging - 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
A2 - Cao, Xiaohuan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 October 2020 through 4 October 2020
ER -