Toward automatic phenotyping of developing embryos from videos

By F. Ning, D. Delhomme, Yann LeCun, F. Piano, L. Eon Bottou, Paolo Emilio Barbano. 2005

291 citations recorded by the source corpus.

Subfields: Aging, Biophysics, Computer Vision and Pattern Recognition, Computational Biology & Bioinformatics

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Citations in the corpus

Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation (2014, methodology): “Joint training of neural-networks and graphical models has been previously reported by Ning et al. [23] for image segmentation, and by various groups in speech and language modeling [6, 22].”
Classifying and segmenting microscopy images with deep multiple instance learning (2015, background): “The same network can also produce an image level classiﬁcation over images of arbitrary size and varying number of cells through a MIL pooling layer.”
Large-Scale FPGA-based Convolutional Networks (2011, background): “…as the robot runs, using labels provided by a short-range stereovision system (see videos at http://www.cs.nyu.edu/~yann/research/lagr), • interesting new applications include image restoration (Jain and Seung, 2008) and image segmentation, particularly for biological images (Ning et al., 2005).”
Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks (2016, methodology): “Consider, if we detect lesions from a volume (as is the case in this work) using the traditional sliding window strategy [24], [32], [33], over 39 millions of 3D patches are sampled in a voxel-wise manner.”
Deep Learning in Medical Imaging: General Overview (2017, methodology): “These include approaches for the segmentation of the lungs (41), tumors and other structures in the brain (42, 43), biological cells and membranes (27, 44), tibial cartilage (45), bone tissue (46), and cell mitosis (47).”
Deep learning in bioinformatics (2016, methodology): “Gene expression regulation [99-104] Anomaly classification [125-132] Brain decoding [164-167] Segmentation [134-140] Anomaly classification [176] Recognition [144-147]”
Deep Learning in Medical Imaging (2019, methodology): “35-37 These approaches used patch-based 2-dimensional CNN techniques and postprocessing in the same way as classical ML.”
Convolutional networks and applications in vision (2010, background): “Interesting new applications include image restoration [24] and image segmentation, particularly for biological images [25].”
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards (2016, background): “This is due to the ability of the CNNs to share smaller-scale filters in each layer, minimizing the number of model parameters.”
Supervised Learning of Image Restoration with Convolutional Networks (2007, methodology): “As mentioned previously, we were inspired by previous research on convolutional networks applied to object recognition, as well as one previous study that used convolutional networks to label and segment regions in microscopic imagery [15, 16, 20].”
A Fast Learning Algorithm for Deep Belief Nets (2006, background): “…that use real-valued units and different sampling schemes are described in Teh et al. (2003) and have been quite successful for modeling the formation of to-pographic maps (Welling et al., 2003), for denoising natural images (Roth and Black, 2005) or images of biological cells (Ning et al., 2005).”
Deep neural networks for anatomical brain segmentation (2015, background): “elegans embryos [17], biological neuron membrane [5], tibial cartilage [19], bone tissue [4] and cell mitosis [6], amongst others.”
3D Convolutional Neural Networks for Human Action Recognition (2010, unknown): “Indeed, this approach has been used to analyze the videos of developing embryos [39].”
Multi-objective convolutional learning for face labeling (2015, methodology): “Other than sequential combination, joint training of CNNs and graphical models have been reported in several papers [15, 17, 23].”
Microscopy cell counting and detection with fully convolutional regression networks (2018, background): “elegans embryos (Ning et al. 2005), and cell segmentation (Ronneberger et al.”