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PAC-Bayes-In-Medical-Imaging

Introduction

The repo for the pre-print work "PAC Bayesian Performance Guarantees for Deep(Stochastic) Networks in Medical Imaging." Available at: https://arxiv.org/abs/2104.05600

Preparation

Prerequisites

  • Python 3.6
  • Pytorch 1.4
  • numpy
  • tqdm
  • pandas
  • PIL

Dataset Preparation

  • Run get_data.sh to retrieve the ISIC2018 challenge data.
  • Run make_split.py to generate a train test split.
  • Run python3 -m src.main **kwargs to train models and compute bounds.

Training

To reproduce the results showed in the fig a, b, c, and d, please run the following scripts.

Fig a

  • sh scripts/fig_a/LW.sh
  • sh scripts/fig_a/LW-PBB.sh
  • sh scripts/fig_a/U-Net.sh
  • sh scripts/fig_a/U-Net-PBB.sh

Fig b

  • sh scripts/fig_b/sigma_prior_0.001.sh
  • sh scripts/fig_b/sigma_prior_0.005.sh
  • sh scripts/fig_b/sigma_prior_0.01.sh
  • sh scripts/fig_b/sigma_prior_0.02.sh
  • sh scripts/fig_b/sigma_prior_0.03.sh
  • sh scripts/fig_b/sigma_prior_0.04.sh
  • sh scripts/fig_b/sigma_prior_0.05.sh

Fig c

  • sh scripts/fig_c/sigma_prior_0.001.sh
  • sh scripts/fig_c/sigma_prior_0.005.sh
  • sh scripts/fig_c/sigma_prior_0.01.sh
  • sh scripts/fig_c/sigma_prior_0.02.sh
  • sh scripts/fig_c/sigma_prior_0.03.sh
  • sh scripts/fig_c/sigma_prior_0.04.sh
  • sh scripts/fig_c/sigma_prior_0.05.sh
  • sh scripts/fig_c/sigma_prior_0.1.sh
  • sh scripts/fig_c/sigma_prior_0.2.sh

Fig d

  • sh scripts/fig_d/LW.sh
  • sh scripts/fig_d/U-Net.sh