Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

This repository contains the reference codes of Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation.

Implemented Models

CNN-RNN-RNN (Liu et al., 2019)
Knowing When to Look (Lu et al., 2017)
Meshed-Memory Transformer (Cornia et al., 2020)
Show, Attend and Tell (Xu et al., 2015)

Supported Datasets

MIMIC-CXR-JPG (Johnson et al., 2019)
Open-i (Demner-Fushman et al., 2016)

Preprocesses

Python Setup

Create a conda environment

$ conda env create -f environment.yml

Resize MIMIC-CXR-JPG

Download MIMIC-CXR-JPG
Make a resized copy of MIMIC-CXR-JPG using resize_mimic-cxr-jpg.py (MIMIC_CXR_ROOT is a dataset directory containing mimic-cxr)
- $ python resize_mimic-cxr-jpg.py MIMIC_CXR_ROOT
Create the sections file of MIMIC-CXR (mimic_cxr_sectioned.csv.gz) with create_sections_file.py
Move mimic_cxr_sectioned.csv.gz to MIMIC_CXR_ROOT/mimic-cxr-resized/2.0.0/

Compute Document Frequencies

Pre-calculate document frequencies that will be used in CIDEr by:

$ python cider-df.py MIMIC_CXR_ROOT mimic-cxr_train-df.bin.gz`

Recognize Named Entities

Pre-recognize named entities in MIMIC-CXR by:

$ python ner_reports.py --stanza-download MIMIC_CXR_ROOT mimic-cxr_ner.txt.gz`

Pre-train CheXpert Image Weights

Download CheXpert Dataset v1.0
Train a CheXpert classification model by:

$ python train_image.py --cuda --epochs 12 --batch-size 16 --eval-interval 65000 --cache-data cache CheXpert-v1.0-small densenet chexpert_densenet

Download Pre-trained Weights

Download pre-trained radiology NLI weights and GloVe embeddings

$ cd resources
$ ./download.sh

Training a Report Generation Model

First, train the Meshed-Memory Transformer model with an NLL loss.

# NLL
$ python train.py --cuda --corpus mimic-cxr --cache-data cache --epochs 32 --batch-size 24 --entity-match mimic-cxr_ner.txt.gz --img-model densenet --img-pretrained chexpert_densenet/model_auc14.dict.gz --bert-score distilbert-base-uncased --corpus mimic-cxr --lr-scheduler trans MIMIC_CXR_ROOT resources/glove_mimic-cxr_train.512.txt.gz out_m2trans_nll

Secondly, further train the model a joint loss using the self-critical RL to achieve a better performance.

# RL with NLL + BERTScore + EntityMatchExact
$ python train.py --cuda --corpus mimic-cxr --cache-data cache --epochs 32 --batch-size 24 --rl-epoch 1 --rl-metrics BERTScore,EntityMatchExact --rl-weights 0.01,0.495,0.495 --entity-match resources/mimic-cxr_ner.txt.gz --baseline-model out_m2trans_nll/model_31-152173.dict.gz --img-model densenet --img-pretrained chexpert_densenet/chexpert_auc14.dict.gz --cider-df mimic-cxr_train-df.bin.gz --bert-score distilbert-base-uncased --lr 5e-6 MIMIC_CXR_ROOT resources/glove_mimic-cxr_train.512.txt.gz out_m2trans_nll-bs-emexact

# RL with NLL + BERTScore + EntityMatchNLI
$ python train.py --cuda --corpus mimic-cxr --cache-data cache --epochs 32 --batch-size 24 --rl-epoch 1 --rl-metrics BERTScore,EntityMatchNLI --rl-weights 0.01,0.495,0.495 --entity-match resources/mimic-cxr_ner.txt.gz --baseline-model out_m2trans_nll/model_31-152173.dict.gz --img-model densenet --img-pretrained chexpert_densenet/chexpert_auc14.dict.gz --cider-df mimic-cxr_train-df.bin.gz --bert-score distilbert-base-uncased --lr 5e-6 MIMIC_CXR_ROOT resources/glove_mimic-cxr_train.512.txt.gz out_m2trans_nll-bs-emnli

Checking Result with TensorBoard

A training result can be checked with TensorBoard.

$ tensorboard --logdir out_m2trans_nll-bs-emnli/log
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.0.0 at http://localhost:6006/ (Press CTRL+C to quit)

Licence

See LICENSE and clinicgen/external/LICENSE_bleu-cider-rouge-spice for details.

sunycl/ifcc