Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

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Deep learning in medical imaging and radiation therapy. (2018, methodology): “316 A wide variety of techniques have been developed for tack-ling the data shortage problem for both the general computer vision and medical image analysis domain.”
On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. (2020, background): “Interpretability methods can be of particular help to leverage understanding of potential non–disease-related imaging information (eg, a center-specific marker on an image [40]) that correlates with a given prognostic status.”
Clinical Text Data in Machine Learning: Systematic Review (2020, background): “Unfortunately, the majority of studies reviewed here were limited to the authors’ host institutions [8,10,12,15,17,22,24,25,28,30-33, 35 , 40, 41, Only 9 studies used data from 2 institutions [36,47,50,56,61,100,103,109,112].”
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020, methodology): “Wood et al. (2020) developed ALARM, an MRI head report classiﬁer on head MRI data using BioBERT (Lee et al., 2019) models trained on 1500 radiologist-labeled reports, and demonstrate improvement over simpler ﬁxed embedding and word2vec-based (Mikolov et al., 2013) models (Zech et al., 2018).”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “[60] Polynomial aggregation and multiparty random masking.”