Multi-class metrics for Tensorflow
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Updated
Sep 20, 2022 - Python
Multi-class metrics for Tensorflow
Train, predict, export and reload a tf.estimator for inference
Gradient accumulation on tf.estimator
Fully supervised, multi-class 3D brain segmentation in T1 MRI using an ensemble of diverse CNN architectures (3D FCN, 3D U-Net) with multi-scale input.
Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).
Distributed Deep Learning Framework on Ray, including tensorflow/pytorch/mxnet
Fully supervised, healthy/malignant prostate detection in multi-parametric MRI (T2W, DWI, ADC), using a modified 2D RetinaNet model for medical object detection, built upon a shallow SEResNet backbone.
Tensorflow estimator implementation of the C3D network
TensorFlow practice using the higher-level APIs
Scripts to practice the basics of TF and Keras while building networks for image classification (CIFAR, MNIST).
OpenAI Glow implementation for TPU/GPU
ResNet for CIFAR with Estimator API and tf.keras.Model class
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