Daniel Haehn PRO
Hi, I am a biomedical imaging and visualization researcher who investigates how computational methods can accelerate biological and medical research.
Assignment 5
PyDicom
load CT volume, slice it, adjust window/level
Assignment 6
3 Lectures
1 Assignment
2 Quizzes
4x Journal Clubs
Nuclear Imaging
Biometrics + Future
Final Recap
MEGAQUIZ
MIDL 2020
Coronal
Super-Resolution
Coronal
Super-Resolution
1. Load Data
2. Setup Network
3. Train Network
4. Predict!
4 Steps
Data
Training
Testing
2
Label
?
Label
But we know the answer!
X_train
y_train
X_test
y_test
Setup Network
NUMBER_OF_CLASSES = 10
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=first_image.shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(NUMBER_OF_CLASSES, activation='softmax'))NUMBER_OF_CLASSES = 10
MNIST
NUMBER_OF_CLASSES = 2Cats vs. Dogs
Setup Network
NUMBER_OF_CLASSES = 10
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=first_image.shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(NUMBER_OF_CLASSES, activation='softmax'))model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])Train Network
9
Training Data
Then we check how well the network predicts the evaluation data!
?
Loss
should go down!
Repeated.. (1 run is called an epoch)
Predict!
Testing Data
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 6 6
7 7 7
8 8 8
9 9 9
Measure how well the CNN does...
Classification
Regression
put samples into different classes
estimate values, "fitting"
0..1 ~ 0..90 degrees
GANs
Generative Adversarial Networks
Create fake images and tune them to look real!
Finetuning
Noise
Ian Goodfellow
Director of Machine Learning
Autoencoders
Compressed representations allow more efficient processing!
80%
Latent Space
Denoising
CS666 Lecture 20
By Daniel Haehn
CS666 Lecture 20
Slides for CS666 Biomedical Signal and Image Processing at UMass Boston. See https://cs666.org!
