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Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

机译：深度卷积神经网络预测大多数脱水图像的长度，周长和重量的应用

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Nicholas Bravata; Dylan Kelly; Jesse Eickholt; Janine Bryan; Scott Miehls; Dan Zielinski;
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1. Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish [J] . Nicholas Bravata, Dylan Kelly, Jesse Eickholt, Ecology and Evolution . 2020,第17期

机译：深度卷积神经网络预测大多数脱水图像的长度，周长和重量的应用
2. Advancements towards selective barrier passage by automatic species identification: applications of deep convolutional neural networks on images of dewatered fish [J] . Eickholt Jesse, Kelly Dylan, Bryan Janine, ICES Journal of Marine Science . 2020,第7a8期

机译：自动种类识别选择性障碍通道的进步：深卷积神经网络对脱水鱼图像的应用
3. Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images [J] . Xiao Mengli, Shen Xiaotong, Pan Wei Genetic epidemiology. . 2019,第3期

机译：深卷积神经网络在蛋白质亚细胞定位分类中的应用
4. SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D Convolutional Neural Networks [C] . Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, IEEE International Symposium on Biomedical Imaging . 2017

机译：SurvivalNet：使用级联的全卷积和3D卷积神经网络从扩散加权磁共振图像预测患者存活率
5. Deep Learning Convolutional Neural Network Applications in Image Analysis to Identify Defects [D] . Umakanth, Srijith Parakkat 2018

机译：深度学习卷积神经网络在图像分析中识别缺陷的应用
6. Applications of deep convolutional neural networks to predict length circumference and weight from mostly dewatered images of fish [O] . Nicholas Bravata, Dylan Kelly, Jesse Eickholt, 2020

机译：深度卷积神经网络预测大多数脱水图像的长度周长和重量的应用
7. Advancements towards selective barrier passage by automatic species identification: applications of deep convolutional neural networks on images of dewatered fish [O] . Jesse Eickholt, Dylan Kelly, Janine Bryan, 2020

机译：自动种类识别选择性障碍通道的进步：深卷积神经网络对脱水鱼图像的应用

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

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