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CS550 Project - PICAI (Prostate Cancer) Challenge

Data Preparation

Data preparation script is used to convert the raw data to convert from MHA Archive to nnU-Net Raw Data Archive and also split the data into splits. This script is provided by Diagnostic Image Analysis Group for this challenge.

All the train, valid data-splits are created by plan_overview.py script, also provided by the Diagnostic Image Analysis Group.

Data Interpretation

SimpleITK library is used to read and process the medical images for detection.

Baseline Model -

We used the monai framework to create the Unet model. The loss function and metrics are provided by the Grand Challenge.

Loss function

  • Focal Loss (Binary Segmentation loss)

Metrics

  • Average Precision (AP)
  • Area Under the Receiver Operating Characteristic curve (AUROC)
  • Overall AI Ranking Metric of the PI-CAI challenge: (AUROC + AP) / 2
  • Precision-Recall (PR) curve
  • Receiver Operating Characteristic (ROC) curve
  • Free-Response Receiver Operating Characteristic (FROC) curve