BenchX: A Unified Benchmark Framework for Medical Vision-Language
  Pretraining on Chest X-Rays
Medical Vision-Language Pretraining (MedVLP) shows promise in learning
generalizable and transferable visual representations from paired and unpaired
medical images and reports. MedVLP can provide useful features to downstream
tasks and facilitate adapting task-specific models to new setups using fewer
examples. However, existing MedVLP methods often differ in terms of datasets,
preprocessing, and finetuning implementations. This pose great challenges in
evaluating how well a MedVLP method generalizes to various clinically-relevant
tasks due to the lack of unified, standardized, and comprehensive benchmark. To
fill this gap, we propose BenchX, a unified benchmark framework that enables
head-to-head comparison and systematical analysis between MedVLP methods using
public chest X-ray datasets. Specifically, BenchX is composed of three
components: 1) Comprehensive datasets covering nine datasets and four medical
tasks; 2) Benchmark suites to standardize data preprocessing, train-test
splits, and parameter selection; 3) Unified finetuning protocols that
accommodate heterogeneous MedVLP methods for consistent task adaptation in
classification, segmentation, and report generation, respectively. Utilizing
BenchX, we establish baselines for nine state-of-the-art MedVLP methods and
found that the performance of some early MedVLP methods can be enhanced to
surpass more recent ones, prompting a revisiting of the developments and
conclusions from prior works in MedVLP. Our code are available at
https://github.com/yangzhou12/BenchX.
6721eb58d3084146fa326496
Yang Zhou
claimed_verified
2024-10-30T09:52:00.565Z
670cc0b60c2a160841c8a343
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/21wJ_Eg4jBvTfaGR4Y7CY.png
ZHOU Yang
user
youngzhou12
6721eb58d3084146fa326497
Tan Li Hui Faith
6721eb58d3084146fa326498
Yanyu Xu
6721eb58d3084146fa326499
Sicong Leng
6721eb58d3084146fa32649a
Xinxing Xu
6721eb58d3084146fa32649b
Yong Liu
6721eb58d3084146fa32649c
Rick Siow Mong Goh
BenchX: A Unified Benchmark Framework for Medical Vision-Language
  Pretraining on Chest X-Rays
Medical Vision-Language Pretraining (MedVLP) shows promise in learning
generalizable and transferable visual representations from paired and unpaired
medical images and reports. MedVLP can provide useful features to downstream
tasks and facilitate adapting task-specific models to new setups using fewer
examples. However, existing MedVLP methods often differ in terms of datasets,
preprocessing, and finetuning implementations. This pose great challenges in
evaluating how well a MedVLP method generalizes to various clinically-relevant
tasks due to the lack of unified, standardized, and comprehensive benchmark. To
fill this gap, we propose BenchX, a unified benchmark framework that enables
head-to-head comparison and systematical analysis between MedVLP methods using
public chest X-ray datasets. Specifically, BenchX is composed of three
components: 1) Comprehensive datasets covering nine datasets and four medical
tasks; 2) Benchmark suites to standardize data preprocessing, train-test
splits, and parameter selection; 3) Unified finetuning protocols that
accommodate heterogeneous MedVLP methods for consistent task adaptation in
classification, segmentation, and report generation, respectively. Utilizing
BenchX, we establish baselines for nine state-of-the-art MedVLP methods and
found that the performance of some early MedVLP methods can be enhanced to
surpass more recent ones, prompting a revisiting of the developments and
conclusions from prior works in MedVLP. Our code are available at
https://github.com/yangzhou12/BenchX.
BenchX is a unified benchmark framework for evaluating MedVLP methods using chest X-ray datasets, enabling consistent comparisons and analyses across various medical tasks.
Medical Vision-Language Pretraining MedVLP BenchX chest X-ray datasets medical tasks parameter selection data preprocessing task adaptation classification segmentation report generation