In chest X-ray (CXR) image analysis, rule-based systems are usually employed
to extract labels from reports, but concerns exist about label quality. These
datasets typically offer only presence labels, sometimes with binary
uncertainty indicators, which limits their usefulness. In this work, we present
MAPLEZ (Medical report Annotations with Privacy-preserving Large language model
using Expeditious Zero shot answers), a novel approach leveraging a locally
executable Large Language Model (LLM) to extract and enhance findings labels on
CXR reports. MAPLEZ extracts not only binary labels indicating the presence or
absence of a finding but also the location, severity, and radiologists'
uncertainty about the finding. Over eight abnormalities from five test sets, we
show that our method can extract these annotations with an increase of 5
percentage points (pp) in F1 score for categorical presence annotations and
more than 30 pp increase in F1 score for the location annotations over
competing labelers. Additionally, using these improved annotations in
classification supervision, we demonstrate substantial advancements in model
quality, with an increase of 1.7 pp in AUROC over models trained with
annotations from the state-of-the-art approach. We share code and annotations.

使用 MAPLEZ 方法，使用基于规则的系统从 CXR 报告中提取和增强发现标签，包括存在性、位置、严重程度以及放射科医生对发现的不确定性，并在分类监督中取得了显著的模型质量提升

利用隐私保护的大型语言模型和多类型标注增强胸部 X 光数据集：提高分类的数据驱动方法

Enhancing chest X-ray datasets with privacy-preserving large language  models and multi-type annotations: a data-driven approach for improved  classification

Federated learning enables building a shared model from multicentre data
while storing the training data locally for privacy. In this paper, we present
an evaluation (called CXR-FL) of deep learning-based models for chest X-ray
image analysis using the federated learning method. We examine the impact of
federated learning parameters on the performance of central models.
Additionally, we show that classification models perform worse if trained on a
region of interest reduced to segmentation of the lung compared to the full
image. However, focusing training of the classification model on the lung area
may result in improved pathology interpretability during inference. We also
find that federated learning helps maintain model generalizability. The
pre-trained weights and code are publicly available at
(this https URL).

本篇论文提出并评估了一个名为 CXR-FL 的基于深度学习的联邦学习方法，用于医学影像分析，证明了联邦学习有助于保持模型的泛化能力以及深度学习模型对于肺部分割数据更为敏感，且其在分类方面的性能对于联邦学习的参数选择有一定程度的依赖。