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
Scripts to practice the basics of TF and Keras while building networks for image classification (CIFAR, MNIST).
OpenAI Glow implementation for TPU/GPU
TensorFlow practice using the higher-level APIs
ResNet for CIFAR with Estimator API and tf.keras.Model class
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