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

Last update: Dec 15, 2022

Related tags

Deep Learning ifcc

Overview

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

The reference code 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)
TieNet (Wang et al., 2018)

Supported Radiology Report Datasets

MIMIC-CXR-JPG (Johnson et al., 2019)
Open-i (Demner-Fushman et al., 2012)

Radiology NLI Dataset

The Radiology NLI dataset (RadNLI) is available at a corresponding PhysioNet project.

Prerequisites

A Linux OS (tested on Ubuntu 16.04)
Memory over 24GB
A gpu with memory over 12GB (tested on NVIDIA Titan X and NVIDIA Titan XP)

Preprocesses

Python Setup

Create a conda environment

$ conda env create -f environment.yml

NOTE : environment.yml is set up for CUDA 10.1 and cuDNN 7.6.3. This may need to be changed depending on a runtime environment.

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

Download Pre-trained Weights

Download pre-trained CheXpert weights, 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 resources/chexpert_auc14.dict.gz --cider-df mimic-cxr_train-df.bin.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

Second, 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 mimic-cxr_ner.txt.gz --baseline-model out_m2trans_nll/model_31-152173.dict.gz --img-model densenet --img-pretrained resources/chexpert_auc14.dict.gz --cider-df mimic-cxr_train-df.bin.gz --bert-score distilbert-base-uncased --lr 5e-6 --lr-step 32 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 mimic-cxr_ner.txt.gz --baseline-model out_m2trans_nll/model_31-152173.dict.gz --img-model densenet --img-pretrained resources/chexpert_auc14.dict.gz --cider-df mimic-cxr_train-df.bin.gz --bert-score distilbert-base-uncased --lr 5e-6 --lr-step 32 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)

Evaluation using CheXbert

NOTE: This evaluation assumes that CheXbert is set up in ./CheXbert.

First, extract reference reports to a csv file.

$ python extract_reports.csv MIMIC_CXR_ROOT/mimic-cxr-resized/2.0.0/mimic_cxr_sectioned.csv.gz MIMIC_CXR_ROOT/mimic-cxr-resized/2.0.0/mimic-cxr-2.0.0-split.csv.gz mimic-imp
$ mv mimic-imp CheXbert/src/

Second, convert generated reports to a csv file. (TEST_SAMPLES is a path to test samples. e.g., out_m2trans_nll-bs-emnli/test_31-152173_samples.txt.gz)

$ python convert_generated.py TEST_SAMPLES gen.csv
$ mv gen.csv CheXbert/src/

Third, run CheXbert against the reference reports.

$ cd CheXbert/src/
$ python label.py -d mimic-imp/reports.csv -o mimic-imp -c chexbert.pth

Fourth, run eval_prf.py to obtain CheXbert scores.

$ cp ../../eval_prf.py . 
$ python eval_prf.py mimic-imp gen.csv gen_chex.csv
2947 references
2347 generated
...
5-micro x.xxx x.xxx x.xxx
5-acc x.xxx

Inferring from a Checkpoint

An inference from a checkpoint can be done with infer.py. (CHECKPOINT is a path to the checkpoint)

$ python infer.py --cuda --corpus mimic-cxr --cache-data cache --batch-size 24 --entity-match mimic-cxr_ner.txt.gz --img-model densenet --img-pretrained resources/chexpert_auc14.dict.gz --cider-df mimic-cxr_train-df.bin.gz --bert-score distilbert-base-uncased --corpus mimic-cxr --lr-scheduler trans MIMIC_CXR_ROOT CHECKPOINT resources/glove_mimic-cxr_train.512.txt.gz out_infer

Pre-trained checkpoints for M2 Transformer can be obtained with a download script.

$ cd checkpoints
$ ./download.sh

Licence

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

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

Related tags

Overview

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

Implemented Models

Supported Radiology Report Datasets

Radiology NLI Dataset

Prerequisites

Preprocesses

Python Setup

Resize MIMIC-CXR-JPG

Compute Document Frequencies

Recognize Named Entities

Download Pre-trained Weights

Training a Report Generation Model

Checking Result with TensorBoard

Evaluation using CheXbert

Inferring from a Checkpoint

Licence

Owner

Code for "FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation".

PyTorch GPU implementation of the ES-RNN model for time series forecasting

LIMEcraft: Handcrafted superpixel selectionand inspection for Visual eXplanations

ColBERT: Contextualized Late Interaction over BERT (SIGIR'20)

DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

Supervised forecasting of sequential data in Python.

This is the repository of shape matching algorithm Iterative Rotations and Assignments (IRA)

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions,spherical coordinates, and intensity

Kernel Point Convolutions

3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

The Codebase for Causal Distillation for Language Models.

[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

Unsupervised clustering of high content screen samples

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Official Pytorch implementation of C3-GAN

Efficient Training of Audio Transformers with Patchout