README.md

Diagnosis

Articles

Dermatologist-level classification of skin cancer with deep neural networks

Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer

Week 1 : Learning Objective

• Data pre-processing: checking for data leakage

• Preprocess images properly for the train, validation and test sets

• Implement a weighted loss function to address class imbalance.

• Set up a pre-trained neural network to make disease predictions on chest x-rays.

• Assignment:

  • Chest X-Ray Medical Diagnosis with Deep Learning

• Labs:

  • Data Exploration & Image Pre-Processing

  • Counting labels and weighted loss function

  • Densenet

  • Patient Overlap and Data Leakage

Week 2 : Learning Objective

• Calculate true positives, true negatives, false positives, false negatives.

• Calculate sensitivity and specificity

• Calculate Positive Predictive Value (PPV) and Negative Predictive Value (NPV).

• Understand confidence intervals, ROC curve, and F1 score.

• Assignment:

  • Evaluation of Diagnostic Models

• Lab :

  • ROC Curve and Threshold

Week 3 : Learning Objective

• Perform image segmentation on 3D MRI data.
• Take random sub-samples from a 3D image.
• Standardize an input image.
• Apply a pre-trained U-Net model.
• Implement a proper loss function for model training (soft dice loss).
• Evaluate model performance by calculating sensitivity and specificity.
• Assignment:
  • Brain Tumor Auto-Segmentation for Magnetic Resonance Imaging (MRI)
• Lab :
  • Explore MRI Data and Labels
  • Extract a Sub Section
  • U-Net Model