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MIC-DKFZ / medicaldetectiontoolkit

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The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

deep-neural-networks deep-learning detection medical-imaging segmentation object-detection medical-image-computing semantic-segmentation 3d-models medical-image-processing mask-rcnn u-net medical-image-analysis pytorch-cnn retina-net 3d-object-detection pytorch-implementation pytorch-deeplearning retina-unet 3d-mask-rcnn
  • Updated Apr 3, 2024
  • Python

Ankit-Kumar-Saini / Coursera_TensorFlow_Advanced_Techniques_Specialization

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Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.

object-detection image-segmentation generative-adversarial-networks auto-encoders saliency-map mask-rcnn variational-autoencoders coursera-specialization retina-net model-interpretability tensorflow2 functional-api custom-model-development custom-loss-functions gradcam-visualization custom-layers gradient-tape-optimization custom-training-loops
  • Updated Jun 9, 2021
  • Jupyter Notebook

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