The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

Last update: Dec 16, 2022

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Deep Learning FFA-IR

Overview

FFA-IR

The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

The framework is inherited from R2Gen.

Data

Our dataset, including all FFA images and annotation files, is available on PhysioNet.

To extract all the files, please first download all the files in FFAIR, and use the command "cat FAIR.tar.gz.* | tar -zxv". Then the name of each directory refers to the case ID, and all the FFA images are provided.

Please put the data and annotation files in 'code/data' directory. Or you can change the code in main.py to fit your own condition.

Requirements

torch==1.5.1
torchvision==0.6.1
opencv-python==4.4.0.42

Training

You can directly run our code by the following:

python main.py

Contact

If you are interested in this dataset or have any questions, please connect us: [email protected].

Owner

Mingjie

Ph.D. Candidate in Monash University

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The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

Related tags

Overview

FFA-IR

Data

Requirements

Training

Contact

Owner

Mingjie

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