Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models

• URL: http://arxiv.org/abs/2306.08000v1
• Date: Tue, 13 Jun 2023 06:26:54 GMT
• Title: Improving Zero-Shot Detection of Low Prevalence Chest Pathologies using Domain Pre-trained Language Models
• Authors: Aakash Mishra, Rajat Mittal, Christy Jestin, Kostas Tingos, Pranav Rajpurkar
• Abstract summary: We evaluate the performance of zero-shot classification models with domain-specific pre-training for detecting low-prevalence pathologies. Even though replacing the weights of the original CLIP-BERT degrades model performance on commonly found pathologies, we show that pre-trained text towers perform exceptionally better on low-prevalence diseases.
• Score: 0.9049664874474734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in zero-shot learning have enabled the use of paired image-text data to replace structured labels, replacing the need for expert annotated datasets. Models such as CLIP-based CheXzero utilize these advancements in the domain of chest X-ray interpretation. We hypothesize that domain pre-trained models such as CXR-BERT, BlueBERT, and ClinicalBERT offer the potential to improve the performance of CLIP-like models with specific domain knowledge by replacing BERT weights at the cost of breaking the original model's alignment. We evaluate the performance of zero-shot classification models with domain-specific pre-training for detecting low-prevalence pathologies. Even though replacing the weights of the original CLIP-BERT degrades model performance on commonly found pathologies, we show that pre-trained text towers perform exceptionally better on low-prevalence diseases. This motivates future ensemble models with a combination of differently trained language models for maximal performance.

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