Paper list of NAS in Medical Imaging

This is an updated list of papers related to NAS in Medical Imaging. For a full list of literature on NAS, one can refer to this useful link:

https://www.automl.org/automl/literature-on-neural-architecture-search/

The papers accepted at conferences and journals are in bold. Hopefully it can provide some guidance towards high-quality papers.

Maintained by Jinnian Zhang; Last update: December 24th 2020

key words: medical; U-Net; UNet; lesion; retina; MICCAI; health; MRI;

• Automatic Data Augmentation for 3D Medical Image Segmentation (Xu et al. 2020; accepted at MICCAI 2020) https://link.springer.com/chapter/10.1007/978-3-030-59710-8_37

• Remote Intelligent Assisted Diagnosis System for Hepatic Echinococcosis (Wang et al. 2020; accepted at International Workshop on Advances in Simplifying Medical Ultrasound, International Workshop on Preterm, Perinatal and Paediatric Image Analysis(ASMUS 2020, PIPPI 2020)) https://link.springer.com/chapter/10.1007/978-3-030-60334-2_1

• Neural Architecture Search for Optimization of Spatial-Temporal Brain Network Decomposition (Li et al. 2020; accepted at International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020) https://link.springer.com/chapter/10.1007/978-3-030-59728-3_37

• NAS-SCAM: Neural Architecture Search-Based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification (Liu et al. 2020; accepted at International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020) https://link.springer.com/chapter/10.1007/978-3-030-59710-8_26

• joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer (Xiao et al. 2020; accepted at International Workshop on Machine Learning in Medical Imaging(MLMI) 2020) https://link.springer.com/chapter/10.1007/978-3-030-59861-7_25

• Neural Architecture Search for Microscopy Cell Segmentation (accepted at International Workshop on Machine Learning in Medical Imaging(MLMI) 2020) https://link.springer.com/chapter/10.1007/978-3-030-59861-7_55

• Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation (Zeng et al. 2020; accepted at ICCAD 2020) https://arxiv.org/abs/2008.07071

• Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound (Huang et al. 2020; accepted at MICCAI 2020) https://arxiv.org/abs/2007.15273

• Neural Architecture Search for Skin Lesion Classification (Kwasigroch et al. 2020; accepted at IEEE Access) https://ieeexplore.ieee.org/document/8950333

• UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation (Ji et al. 2020) https://arxiv.org/abs/2009.07501

• Automatically Searching for U-Net Image Translator Architecture (Shu and Wang. 2020) https://arxiv.org/abs/2002.11581

• Genetic U-Net: Automatically Designing Lightweight U-shaped CNN Architectures Using the Genetic Algorithm for Retinal Vessel Segmentation (Wei and Fan 2020) https://arxiv.org/abs/2010.15560

• ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel (Fan et al. 2020) https://arxiv.org/abs/2001.06678

• MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation (Yan et al. 2020) https://arxiv.org/abs/2007.06151

• An Intelligent Analysis Framework for Clinical-Translational MRI Research (Yang. 2020) https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ACCESSION_NUM:case1592254585828664

• Enhanced MRI Reconstruction Network using Neural Architecture Search (Huange et al. 2020) https://arxiv.org/abs/2008.08248

• Modeling Task-based fMRI Data via Deep Belief Network with Neural Architecture Search (Qiang et al. 2020; accepted at Computerized Medical Imaging and Graphics) https://www.sciencedirect.com/science/article/abs/pii/S0895611120300501

• Optimize CNN Model for FMRI Signal Classification Via Adanet-Based Neural Architecture Search (Dai et al. 2020; accepted at IEEE ISBI) https://ieeexplore.ieee.org/abstract/document/9098574

• AdaEn-Net: An Ensemble of Adaptive 2D-3D Fully Convolutional Networks for Medical Image Segmentation (Baldeon Calisto and Lai-Yuen. 2020; accepted at Neural Networks) https://www.sciencedirect.com/science/article/pii/S0893608020300848

• ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection (Jiang et al. 2020) https://arxiv.org/abs/2003.08770

• Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search (Peng et al. 2020) https://arxiv.org/abs/2007.06002

• C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation (Yu et al. 2019) https://arxiv.org/abs/1912.09628

• Scalable Neural Architecture Search for 3D Medical Image Segmentation (Kim et al. 2019; accepted at MICCAI’19) https://arxiv.org/abs/1906.05956

• Neural Architecture Search for Adversarial Medical Image Segmentation (Dong et al. 2019; accepted at MICCAI’19) https://link.springer.com/chapter/10.1007/978-3-030-32226-7_92

• Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation (Yang et al. 2019; accepted at MICCAI’19) https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1

• Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study (Faes et al. 2019; accepted at The Lancet Digital Health) https://www.sciencedirect.com/science/article/pii/S2589750019301086

• Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data (Quiang et al. 2019; accepted at MMMI’19) https://link.springer.com/chapter/10.1007/978-3-030-37969-8_4

• Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation (Bae et al. 2019; accepted at MICCAI’19) https://arxiv.org/abs/1909.00548

• Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net (Zhang et al. 2019; accepted at MICCAI’19) https://link.springer.com/chapter/10.1007/978-3-030-32248-9_83

• Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation (Calisto and Lai-Yuen. 2019; accepted at SPIE Medical Imaging’20) https://arxiv.org/abs/1907.11587

• V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation (Zhu et al. 2019) https://arxiv.org/abs/1906.02817

• AdaResU-Net: Multiobjective Adaptive Convolutional Neural Network for Medical Image Segmentation (Baldeon-Calisto and Lai-Yuen. 2019.; accepted at Neurocomputing) https://www.sciencedirect.com/science/article/pii/S0925231219304679

• NAS-Unet: Neural Architecture Search for Medical Image Segmentation (Weng et al. 2019; accepted at IEEE Access) https://ieeexplore.ieee.org/document/8681706

• Deep Evolutionary Networks with Expedited Genetic Algorithm for Medical Image Denoising (Liu et al. 2019; accepted at Medical Image Analysis) https://www.sciencedirect.com/science/article/abs/pii/S1361841518307734

• Automatically Designing CNN Architectures for Medical Image Segmentation (Mortazi and Bagci 2018) https://arxiv.org/abs/1807.07663