Projects in Medical Imaging with TensorFlow 2.X and Keras

Photo by Online Marketing on Unsplash

Project Steps:

Step-1:
Extract labels from the given csv file and images from dicom files.
— Use Python Augmentor Package to create augmented images for 1s, since positive cases are very few and I am dealing with imbalanced dataset.
( Note: It is important to not use augmentation that will distort the images excessively)

Metrics for Performance Evaluation
GradCAM (The high-lighted areas are where the model looks at when making predictions)

For Future Study and Improvements:

There are definitely rooms for improvements. Opportunities that I have in mind include:

  1. Select different models, or search for published state-of-the-art models
  2. Set up appropriate class weights or build custom loss function that give more attention to 1s (positives)
  3. Clean up the data and ensure that the dataset does not include any distorted or tilted images. (Some images are not horizontally aligned)

Project Steps:

Step-1:
Extract bounding boxes labels from the given csv file and images from dicom files.

Results:

Below are the results after 63 epochs of training. I have set up an EarlyStopping callback that monitored on ‘val_loss’ to prevent overfitting. My results are shown as below:

Number of predictions where iou > threshold(0.5): 29
Number of predictions where iou < threshold(0.5): 3

For Future Study and Improvements:

There are definitely rooms for improvements. Opportunities that I have in mind include:

  1. Select different models, or search for published state-of-the-art models
  2. Changing a loss function to improve overall IoU score
  3. If RAM allowed, increasing the batch size for a more accurate update in gradients
  4. Use TensorFlow Object Detection API (Results are posted on GitHub)
credit: ai.standford.edu

Project Steps:

Step-1:
Extract mri and segmentation labels using nibabel.

Results:

Ground Truth

Ground Truth of Left Atrium
FCN-8 (Left) //UNET (Right)

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