Medical Report Generation through Radiology Images: An Overview

Ramirez-Alonso, Graciela; Prieto-Ordaz, Olanda; López-Santillán, Roberto; Montes-y-Gómez, Manuel

doi:10.1109/tla.2022.9757742

Cited by 5 publications

(2 citation statements)

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“…In addition, DL is also widely used in medical image reconstruction [ 38 , 39 ], medical image report generation [ 40 ], and other tasks. Deep learning provides important theoretical basis and technical support for intelligent medicine.…”

Section: Related Workmentioning

confidence: 99%

Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention

Wang

et al. 2023

Computational Intelligence and Neuroscience

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Diabetic retinopathy (DR) is a common retinal vascular disease, which can cause severe visual impairment. It is of great clinical significance to use fundus images for intelligent diagnosis of DR. In this paper, an intelligent DR classification model of fundus images is proposed. This method can detect all the five stages of DR, including of no DR, mild, moderate, severe, and proliferative. This model is composed of two key modules. FEB, feature extraction block, is mainly used for feature extraction of fundus images, and GPB, grading prediction block, is used to classify the five stages of DR. The transformer in the FEB has more fine-grained attention that can pay more attention to retinal hemorrhage and exudate areas. The residual attention in the GPB can effectively capture different spatial regions occupied by different classes of objects. Comprehensive experiments on DDR datasets well demonstrate the superiority of our method, and compared with the benchmark method, our method has achieved competitive performance.

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Section: Related Workmentioning

confidence: 99%

Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention

Wang

et al. 2023

Computational Intelligence and Neuroscience

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“…Hospitals around the world heavily rely on medical imaging, which provides valuable insights for disease diagnosis and treatment planning [1,2]. However, it is crucial for the radiologist to thoroughly examine the medical images in order to provide comprehensive findings and interpretations [3]. The sample chest x-ray images with the associated unstructured medical text data is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays

Shetty

Ananthanarayana

Mahale

2023

IJE

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Radiology report generation is a critical task for radiologists, and automating the process can significantly simplify their workload. However, creating accurate and reliable radiology reports requires radiologists to have sufficient experience and time to review medical images. Unfortunately, many radiology reports end with ambiguous conclusions, resulting in additional testing and diagnostic procedures for patients. To address this, we proposed an encoder-decoder-based deep learning framework that utilizes chest X-ray images to produce diagnostic radiology reports. In our study, we have introduced a novel text modelling and visual feature extraction strategy as part of our proposed encoder-decoder-based deep learning framework. Our approach aims to extract essential visual and textual information from chest X-ray images to generate more accurate and reliable radiology reports. Additionally, we have developed a dynamic web portal that accepts chest X-rays as input and generates a radiology report as output. We conducted an extensive analysis of our model and compared its performance with other state-of-the-art deep learning approaches. Our findings indicate significant improvement achieved by our proposed model compared to existing models, as evidenced by the higher BLEU scores (BLEU1 = 0.588, BLEU2 = 0.4325, BLEU3 = 0.4017, BLEU4 = 0.3860) attained on the Indiana University Dataset. These results underscore the potential of our deep learning framework to enhance the accuracy and reliability of radiology reports, leading to more efficient and effective medical treatment.

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A Survey of Deep Learning Techniques and Applications in Bioengineering: A Latin American Perspective

Comas,

Meschino,

Amalfitano

et al. 2024

IFMBE Proceedings

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Medical Report Generation through Radiology Images: An Overview

Cited by 5 publications

References 0 publications

Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention

Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention

Cross-modal Deep Learning-based Clinical Recommendation System for Radiology Report Generation from Chest X-rays

A Survey of Deep Learning Techniques and Applications in Bioengineering: A Latin American Perspective

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