Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results

TL;DR

This paper introduces a Bi-CNN model for interpreting and classifying mammogram and chest X-ray reports, aiming to improve database organization, retrieval, and diagnostic accuracy.

Contribution

The paper presents a novel Bi-CNN approach for radiology report interpretation, outperforming traditional machine learning methods in classification tasks.

Findings

Bi-CNN outperforms random forest and SVM.

Helps organize and retrieve radiology reports efficiently.

Potential to reduce diagnostic errors.

Abstract

Radiology reports are an important means of communication between radiologists and other physicians. These reports express a radiologist's interpretation of a medical imaging examination and are critical in establishing a diagnosis and formulating a treatment plan. In this paper, we propose a Bi-directional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports based on the basis of chest pathology. The proposed approach helps to organize databases of radiology reports, retrieve them expeditiously, and evaluate the radiology report that could be used in an auditing system to decrease incorrect diagnoses. Our study revealed that the proposed Bi-CNN outperforms the random forest and the support vector machine methods.