Att-MoE: Attention-based Mixture of Experts for nuclear and cytoplasmic segmentation

Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 1097 Long, 2015, Fully convolutional networks for semantic segmentation, 3431 Ren, 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 91 Wang, 2016, Stct: Sequentially training convolutional networks for visual tracking, 1373 J. Johnson, A. Karpathy, L. Fei-Fei, Densecap: Fully convolutional localization networks for dense captioning, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. Jacobs, 1991, Adaptive mixtures of local experts, Neural Computation, 3, 79, 10.1162/neco.1991.3.1.79 S. Kumagai, K. Hotta, T. Kurita, Mixture of counting cnns: Adaptive integration of cnns specialized to specific appearance for crowd counting, arXiv preprint arXiv:1703.09393. Hiramatsu, 2018, Cell image segmentation by integrating multiple cnns, 2205 Wang, 2019, Benchmark on automatic six-month-old infant brain segmentation algorithms: the iseg-2017 challenge, IEEE Transactions on Medical Imaging, 38, 2219, 10.1109/TMI.2019.2901712 Awate, 2019, Estimating uncertainty in mrf-based image segmentation: A perfect-mcmc approach, Medical Image Analysis, 55, 181, 10.1016/j.media.2019.04.014 Sui, 2017, Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks, Neurocomputing, 237, 332, 10.1016/j.neucom.2017.01.023 Y. He, W. Jiao, Y. Shi, B. Zhao, W. Zou, J. Lian, Y.-M. Zhu, Y. Zheng, Segmenting diabetic retinopathy lesions in multispectral images using low-dimensional spatial-spectral matrix representation, IEEE Journal of Biomedical and Health Informatics. Otsu, 1979, A threshold selection method from gray-level histograms, IEEE Ttransactions on Systems, Man, and Cybernetics, 9, 62, 10.1109/TSMC.1979.4310076 Yang, 2006, Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy, IEEE Transactions on Circuits and Systems I: Regular Papers, 53, 2405, 10.1109/TCSI.2006.884469 Ali, 2012, An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery, IEEE Transactions on Medical Imaging, 31, 1448, 10.1109/TMI.2012.2190089 Xu, 1998, Snakes, shapes, and gradient vector flow, IEEE Transactions on Image Processing, 7, 359, 10.1109/83.661186 P. Guo, A. Evans, P. Bhattacharya, Segmentation of nuclei in digital pathology images, in: 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), IEEE, 2016, pp. 547–550. Qi, 2014, Dense nuclei segmentation based on graph cut and convexity–concavity analysis, Journal of Microscopy, 253, 42, 10.1111/jmi.12096 Al-Kofahi, 2009, Improved automatic detection and segmentation of cell nuclei in histopathology images, IEEE Transactions on Biomedical Engineering, 57, 841, 10.1109/TBME.2009.2035102 Deoni, 2007, Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 t, Neuroimage, 34, 117, 10.1016/j.neuroimage.2006.09.016 Badrinarayanan, 2017, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481, 10.1109/TPAMI.2016.2644615 Chen, 2017, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834, 10.1109/TPAMI.2017.2699184 Lin, 2017, Refinenet: Multi-path refinement networks for high-resolution semantic segmentation, 1925 Zhao, 2017, Pyramid scene parsing network, 2881 Ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, 234 Graham, 2018, Sams-net: Stain-aware multi-scale network for instance-based nuclei segmentation in histology images, 590 Kumar, 2017, A dataset and a technique for generalized nuclear segmentation for computational pathology, IEEE Transactions on Medical Imaging, 36, 1550, 10.1109/TMI.2017.2677499 Naylor, 2018, Segmentation of nuclei in histopathology images by deep regression of the distance map, IEEE Transactions on Medical Imaging, 38, 448, 10.1109/TMI.2018.2865709 F. Mahmood, D. Borders, R. Chen, G.N. McKay, K.J. Salimian, A. Baras, N.J. Durr, Deep adversarial training for multi-organ nuclei segmentation in histopathology images, IEEE Transactions on Medical Imaging. Raza, 2017, Mimo-net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images, 337 Raza, 2019, Micro-net: A unified model for segmentation of various objects in microscopy images, Medical Image Analysis, 52, 160, 10.1016/j.media.2018.12.003 He, 2016, Deep residual learning for image recognition, 770 S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167. Anderson, 2018, Bottom-up and top-down attention for image captioning and visual question answering, 6077 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Łstrok. Kaiser, I. Polosukhin, Attention is all you need, in: Advances in neural information processing systems, 2017, pp. 5998–6008. Xiong, 2018, Multi-channel encoder for neural machine translation Wang, 2017, Residual attention network for image classification, 3156 Fu, 2017, Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition, 4438 O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y. Hammerla, B. Kainz, et al., Attention u-net: Learning where to look for the pancreas, arXiv preprint arXiv:1804.03999. Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, 15, 1929 He, 2015, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 1026 D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, corr abs/1412.6980 (2014) (2014). Everingham, 2015, The pascal visual object classes challenge: A retrospective, International Journal of Computer Vision, 111, 98, 10.1007/s11263-014-0733-5 H. Gao, P. Xue, W. Lin, A new marker-based watershed algorithm, in: 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No. 04CH37512), vol. 2, IEEE, 2004, pp. II–81. Chen, 2017, Dcan: Deep contour-aware networks for object instance segmentation from histology images, Medical Image Analysis, 36, 135, 10.1016/j.media.2016.11.004