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PathologyBERT - Pre-trained Vs. A New Transformer Language Model for Pathology Domain
Thiago Santos1, Amara Tariq2, Susmita Das3
1Emory University, Department of Computer Science, Atlanta, Georgia, USA.
Summary
PathologyBERT, a new language model trained on pathology reports, improves cancer research by enhancing Natural Language Understanding and breast cancer diagnosis classification. This specialized model addresses limitations of general language models in pathology data mining.
Area of Science:
- Computational pathology
- Bioinformatics
- Medical Natural Language Processing (NLP)
Background:
- Pathology text mining is crucial for big data cancer research but faces challenges due to reporting variability and evolving cancer definitions.
- Existing general language models often underperform in specialized domains like pathology due to unique terminology.
- A pathology-specific language model is needed to support advanced data-mining applications in cancer research.
Purpose of the Study:
- To develop and evaluate PathologyBERT, a novel masked language model pre-trained on a large corpus of histopathology reports.
- To assess the performance of PathologyBERT compared to general language models on pathology-related Natural Language Understanding (NLU) tasks.
- To demonstrate the utility of pathology-specific pre-training for improving cancer diagnosis classification.
Main Methods:
- Pre-trained a masked language model (PathologyBERT) on 347,173 histopathology specimen reports.
- Evaluated PathologyBERT's performance on Natural Language Understanding (NLU) benchmarks.
- Assessed PathologyBERT's effectiveness in a Breast Cancer Diagnose Classification task.
- Compared performance against general, non-specialized language models.
Main Results:
- PathologyBERT demonstrated significant performance improvements on Natural Language Understanding (NLU) tasks.
- The model achieved enhanced accuracy in Breast Cancer Diagnose Classification compared to general language models.
- Pre-training on a specialized pathology corpus positively impacted model performance.
Conclusions:
- Pre-training transformer models on pathology-specific corpora, as exemplified by PathologyBERT, leads to superior performance in pathology-related NLP tasks.
- PathologyBERT provides a valuable resource for advancing 'big data' cancer research, including treatment selection and clinical trial screening.
- This specialized language model addresses the need for robust NLP tools in the field of computational pathology.
