Embedded Files
October 2022
https://courses.nvidia.com/courses/course-v1:DLI+L-FX-04+V2/
https://colab.research.google.com/drive/1YuQmW9SPLkn_zuvF2xopX4AvH2GdA2hq#scrollTo=C2k6Qi1dZNnL
Image classification - e.g. picture of a chair
Omage classification + localization - bounding box of object in image
Object detection - chain and bin
Image segmentation - pixel labelling
semantic segmentation - separate not by color but by class
Look at medical imaging and locate the left ventricle i.e. for each pixel is it part of the LV
Can make Keras models as sequential or functional
e.g.
model = tf.keras.models.Sequenctial([Flatten(input_shape=[256, 256, 1]), Dense(64, activation="relu"), Dense(256 * 256 * 2, activation="softmax"), Reshape((256, 256, 2))])
Prepare model - TFRecords
Build the model
Train the model
Event the model - DICE, tensorboard
Dataset
Cardiac MRI short-axis (SAX) scans
https://www.kaggle.com/competitions/second-annual-data-science-bowl
256 x 256 grayscale DICOM
output is 256 x 256 x 2 (LV or not)
234 images in training and validation of 26 images
images and contours have been extracted from the raw data for TF as TF data records
task 1
FC with one hidden layer (512) with sparse_softmax_cross_entropy_with_logits - softmax of the output then cross entropy against the correct labels - assign prob for each class and sorftmax so sum is = 1 - cross entropy means strongly punish wrong predictions if predictions 0% change but happens have a near infinite loss.
task 2
CNN layers - capture larger receptive fields. down-sampling method to retain info while eliminating some complexity
Transpose convolution - single pixel to multiple pixels.
max pooling - pick largest value
DICE metric - area of prediction area over area of actual value - perfect 1.0 - worse - 0.0
Task 3
experiment with learning_rate, decay_rate, decay_steps, num_epoch
Solution UNET
upscale stetch out the image
pairs conv going down with conv going up
each conv produces a new image. take image and stack with conv going up.
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