# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## Appendix A
Intell._**2019**, _41_, 1559-1572. [CrossRef] [PubMed]
* [15] [PERSON]; [PERSON] Very Deep Convolutional Networks for Large-Scale Image Recognition. In Proceedings of the 3 rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7-9 May 2015. * [16] [PERSON]; [PERSON]; [PERSON]; [PERSON] Image Segmentation Based on Improved Unet. _J. Phys. Conf. Ser._**2021**, _1815_, 012018. [CrossRef]
* [17] [PERSON]; [PERSON]; [PERSON]; [PERSON] Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation. _Front. Neuroinform._**2022**, _16_. [CrossRef] [PubMed]
* [18] [PERSON]; [PERSON]; [PERSON]; [PERSON] U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications. _IEEE Access_**2021**, \(9\), 82031-82057. [CrossRef]
* [19] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Improved U-Net based insulator Image Segmentation method based on attention mechanism. _Energy Rep._**2021**, \(7\), 210-217. * [20] [PERSON]; [PERSON] COVID-19 lung CT Image Segmentation using deep learning methods: U-Net versus SegNet. _BMC Med. Imaging_**2021**, _21_, 19. [CrossRef]
* [21] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images. In _Proceedings of the Medical Image Computing and Computer Assisted Intervention--MICCAI 2019: 22 nd International Conference, Shenzhen, China, 13-17 October 2019, Proceedings, Part III;_ Springer: Berlin/Heidelberg, Germany, 2019; pp. 829-838. [CrossRef]
* [22] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes. In _Proceedings of the Machine Learning in Medical Imaging: 9 th International Workshop, MLMI 2018, Held in conjunction with MICCAI 2018, Granada, Spain, 16 September 2018;_ Springer: Berlin/Heidelberg, Germany, 2018. * [23] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Automatic detection of coronary artery stenosis by convolutional neural network with temporal constraint. _Comput. Biol. Med._**2020**, _118_, 103657. [CrossRef]
* [24] [PERSON]; [PERSON]; [PERSON]; [PERSON] T-Net: Nested encoder-decoder architecture for the main vessel segmentation in coronary angiography. _Neural Netw._**2020**, _128_, 216-233. [CrossRef]
* [25] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In _International Conference on Medical Image Computing and Computer-Assisted Intervention_; Springer: Cham, Switzerland, 2016. [CrossRef]
* [26] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] 3D image reconstruction for comparison of algorithm database: A patient specific anatomical and medical image database. _Tech. Rep. IRCAD_**2010**. Available online: [[http://www-sop.inria.fr/geometric/events/wam/abstract-ircad.pdf](http://www-sop.inria.fr/geometric/events/wam/abstract-ircad.pdf)]([http://www-sop.inria.fr/geometric/events/wam/abstract-ircad.pdf](http://www-sop.inria.fr/geometric/events/wam/abstract-ircad.pdf)) (accessed on 28 September 2022). * _Bishop and Pattern Recognition and Machine Learning_ (2006) [PERSON] Pattern Recognition and Machine Learning. In _Information Science and Statistics_; Springer: New York, NY, USA, 2006; Volume 4, p. 738. [CrossRef]
* _Keras API_ (2022) Keras API. Available online: [[https://keras.io/api/](https://keras.io/api/)]([https://keras.io/api/](https://keras.io/api/)) (accessed on 28 September 2022). * _[PERSON] (1995)_ [PERSON]; [PERSON] The influence of the sigmoid function parameters on the speed of backpropagation learning. In _Proceedings of the From Natural to Artificial Neural Computation_; [PERSON], [PERSON], Eds. ; Springer: Berlin/Heidelberg, Germany, 1995; pp. 195-201. * _[PERSON] (2015)_ [PERSON]; [PERSON] Adam: A Method for Stochastic Optimization.