Refactoring of Code, Proper Folder Structure and Add new codes uptill progress about cellseg HOT 31 CLOSED

Working on GRAD-CAM version 2. Merged: keras-team/keras-io#399

Need some initial motivation from here: https://github.com/YassineYousfi/comma10k-baseline
Also need to look how the robust model performs on these datasets:

• https://www.cityscapes-dataset.com/
• https://github.com/commaai/comma10k
• https://www.mapillary.com/dataset/vistas?pKey=8BV5FtKXHwgZBSFY9Bfc5g&lat=20&lng=0&z=1.5
• Stanford BG http://dags.stanford.edu/projects/scenedataset.html
• PASCAL VOC http://host.robots.ox.ac.uk/pascal/VOC/

from cellseg.

Hey @heraldofsolace I forgot about this task... is this done?
https://colab.research.google.com/drive/16LMsoN0y__pAEVcrqzIb9bJ8y2Fogv7_?usp=sharing

Problem: Classification Model with the following specifications

Task:

• Use data generator from Keras to do data augmentation with a batch size of 24, probably in Google Colab for now.
• Make sure to find/ check the data distribution (using histograms from matplotlib) first, and if there is any class imbalance and stuff, then we have to take necessary actions, and ask for help.
• Split the data to 80-10-10 (80% training, 10% validation and 10% test).
• Record Precision, Recall, Accuracy, Loss, F1 score on datasets, i.e., training, validation and test sets. This can be done using scikit learn.
• Plot all the graphs obtained and display the same.
• Make a confusion matrix plot it using Matplotlib

TODO

• K-Fold cross validation

from cellseg.

Working on it but I feel dumb lol

from cellseg.

from cellseg.

https://colab.research.google.com/drive/16LMsoN0y__pAEVcrqzIb9bJ8y2Fogv7_?usp=sharing
A very simple skeleton is here, but it needs to be modified a lot

from cellseg.

Why? LOL

Well, it's your model

from cellseg.

Oh, so it won't obey you 🌚

from cellseg.

🥺

from cellseg.

Yes

from cellseg.

Also need to build slides, deadline - 14 th March. Need to discuss about that soon...

from cellseg.

@heraldofsolace Probably suggest a good name for this repo xD

from cellseg.

Lol Tamal Mj's Laptop's CUDA just messed up for some reason, I need to fix that tomorrow... Let's see. Studying few papers today.

from cellseg.

from cellseg.

Lol

from cellseg.

In the set "ig" has 4 different types of things "IG", "MMY", "MY", "PMY" and Neutrphil has 3 different types "BNE", "SNE" and "Neutrophil"

from cellseg.

I have seen that before, thought that 8 classes will be good, but eventually we need them too. What would be better? Having a classifier to screen the first 8 classes first and then from that we will use different screening techniques? Or what do you suggest?

This is the cell glossary we will get from the slides

Blast (Bl)
Promyelocyte (PM)
Myelocyte (My)
Metamyelocyte (Me)
Band form  (Band) (Not a cell)
Neutrophil (N)
Eosinophil (Eo)
Basophil (Ba)
Lymphocyte (L)
Monocyte (Mo)
Nucleated RBC (NRBC)

from cellseg.

from cellseg.

I was thinking, maybe take them all as separate classes and separately augment them until they reach 2000 samples or something and combine into one dataset.

from cellseg.

So let's do that then. What techniques will you use?

• color jittering
• flips
• zoom
• blurring(?)
• maybe noise (?)
• maybe (small) random crop of some image into a particular image
• rotation
• affine (?)

I was even thinking of doing an ensemble with polar transformation and FFT, but that will be too much for now

from cellseg.

Let's first write the baseline code and then we'll add more augmentation as needed.

from cellseg.

So, the generator doesn't actually balance the classes. Using the generator we can't control the number of samples in classes. So we have two options -

1. Accept the imbalance and set the model weight accordingly instead with augmentation.
2. Use a dummy training run with augmentation to generate augmented image and create a larger dataset.

I think the 2nd approach would not be good as there is huge imbalance and some of the classes would just be full of slightly different images.

from cellseg.

from cellseg.

Ok

from cellseg.

Aniket, If you have some scripts to run related to the project (say 100 epochs), you can give me.

from cellseg.

https://colab.research.google.com/drive/1s1WgkH0DG2BPHJnG11UTSBLRz0h1CGms?usp=sharing

It predicts everything as BNE lol

from cellseg.

Why? LOL

from cellseg.

def Model_V2_Gradcam(H,W,C):

    input_layer = tf.keras.Input(shape=(H, W, C))
    x_1 = tf.keras.layers.Conv2D(16, 3, activation='relu', strides=(1, 1), name="conv_16_1", padding='same', kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(input_layer)
    x_2 = tf.keras.layers.Conv2D(16, 3, activation='relu', strides=(1, 1), name="conv_16_2", padding='same', kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(x_1)
    # x_4 = tf.keras.layers.Conv2D(16, 3, activation='relu', strides=(1, 1), name="conv_64_21", padding='same')(add([x_3,x_1]))
    x_3 = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool3")(x_2)
    x_4 = tf.keras.layers.Conv2D(32, 3, activation='relu', strides=(1, 1), name="conv_32_1", padding='same', kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(x_3)
    x_5 = tf.keras.layers.Conv2D(32, 3, activation='relu', strides=(1, 1), name="conv_32_2", padding='same', kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(x_4)

    x_6 = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool4")(x_5)
    x_7 = tf.keras.layers.Conv2D(64, 3, activation='relu', strides=(1, 1), name="conv_64_1", padding='same', kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(x_6)
    x_8 = tf.keras.layers.Conv2D(64, 3, activation='relu', strides=(1, 1), name="conv_64_2", padding='same', kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(x_7)
    x = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool5")(x_8)
    x = tf.keras.layers.Conv2D(64, 3, activation='relu', strides=(2, 2), name="conv_64_3", kernel_initializer = 'he_normal', kernel_regularizer=l2(1e-4))(x)
    x = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool6")(x)
    x = tf.keras.layers.Flatten(name="flatten")(x)
    x = tf.keras.layers.Dropout(0.15, name="dropout_3")(x)
    x = tf.keras.layers.Dense(256, activation='relu', name="dense_64")(x)
    x = tf.keras.layers.Dense(N_LABELS, activation='softmax', name="output_layer")(x)

    model = tf.keras.models.Model(inputs=input_layer, outputs=x)
    return model

model = Model_V2_Gradcam(H=360, W=360, C=3)

model.compile(optimizer='adam', loss='categorical_crossentropy',
            metrics= ['accuracy'])
model.summary()

from cellseg.

I told you it needs multiple screening models, one model needs to be too deep to understand the minor variations between the classes, so ends up doing nothing

from cellseg.

https://www.kaggle.com/paultimothymooney/identify-blood-cell-subtypes-from-images

from cellseg.

Oof

from cellseg.

Why? LOL

Well, it's your model

Looks like the primitive one should work better. Not tested though.

def Model_V1_Gradcam(H,W,C):
    input_layer = tf.keras.Input(shape=(H, W, C))
    x = tf.keras.layers.Conv2D(32, 3, activation='relu', strides=(2, 2), name="conv_32")(input_layer)
    x = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool1")(x)
    x = tf.keras.layers.Conv2D(64, 3, activation='relu', strides=(2, 2), name="conv_64")(x)
    x = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool2")(x)
    x = tf.keras.layers.Conv2D(64, 3, activation='relu', strides=(2, 2), name="conv_64_2")(x)
    x = tf.keras.layers.MaxPooling2D((2, 2), name="max_pool3")(x)
    
    x = tf.keras.layers.Flatten(name="flatten")(x)
    x = tf.keras.layers.Dense(512, activation='relu', name="dense_512")(x)
    x = tf.keras.layers.Dropout(0.5, name="dropout_1")(x)
    x = tf.keras.layers.Dense(512, activation='relu', name="dense_256")(x)
    x = tf.keras.layers.Dropout(0.5, name="dropout_2")(x)
    x = tf.keras.layers.Dense(128, activation='relu', name="dense_64")(x)
    x = tf.keras.layers.Dropout(0.5, name="dropout_3")(x)
    
    x = tf.keras.layers.Dense(N_LABELS, activation='softmax', name="output_layer")(x)
    #x = tf.keras.layers.Reshape((1, N_LABELS))(x)
    
    model = tf.keras.models.Model(inputs=input_layer, outputs=x)
    return model

model = Model_V1_Gradcam(H=360, W=360, C=3)

model.compile(optimizer='adam', loss='categorical_crossentropy',
            metrics= ['accuracy'])
model.summary()

from cellseg.