Accurately labeling biomedical data presents a challenge. Traditional
semi-supervised learning methods often under-utilize available unlabeled data.
To address this, we propose a novel reliability-based training data cleaning
method employing inductive conformal prediction (ICP). This method capitalizes
on a small set of accurately labeled training data and leverages ICP-calculated
reliability metrics to rectify mislabeled data and outliers within vast
quantities of noisy training data. The efficacy of the method is validated
across three classification tasks within distinct modalities: filtering
drug-induced-liver-injury (DILI) literature with title and abstract, predicting
ICU admission of COVID-19 patients through CT radiomics and electronic health
records, and subtyping breast cancer using RNA-sequencing data. Varying levels
of noise to the training labels were introduced through label permutation.
Results show significant enhancements in classification performance: accuracy
enhancement in 86 out of 96 DILI experiments (up to 11.4%), AUROC and AUPRC
enhancements in all 48 COVID-19 experiments (up to 23.8% and 69.8%), and
accuracy and macro-average F1 score improvements in 47 out of 48 RNA-sequencing
experiments (up to 74.6% and 89.0%). Our method offers the potential to
substantially boost classification performance in multi-modal biomedical
machine learning tasks. Importantly, it accomplishes this without necessitating
an excessive volume of meticulously curated training data.

我们提出了一种基于可靠性的训练数据清洗方法，利用归纳性依从预测 (ICP) 计算的可靠性度量来纠正大量嘈杂的训练数据中的标签错误和异常值，验证了该方法在三个分类任务中的有效性，显示出显著的分类性能提升，无需过多精心策划的训练数据。