Skip to content

Latest commit

 

History

History
37 lines (23 loc) · 803 Bytes

README.md

File metadata and controls

37 lines (23 loc) · 803 Bytes

PAC-Bayes-In-Medical-Imaging

Introduction

The repo for the work "PAC Bayesian Performance Guarantees for Deep(Stochastic) Networks in Medical Imaging." Accepted to MICCAI 2021. 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 all results run the following scripts:

  • sh scripts/run-lw.sh
  • sh scripts/run-rn.sh
  • sh scripts/run-un.sh

To run individual experiments, please check the comments which identify each subcall with the correct experiment.