Deep learning in generating radiology reports: A survey

By Maram Mahmoud A. Monshi, Josiah Poon, Yuk Ying Chung. 2020

180 citations recorded by the source corpus.

Highlights • Deep Learning algorithms showed promising results in generating radiology reports.• We categorize state of the art models into three levels: word, sentence and paragraph.• We review the publicly available datasets of radiology images and linked reports.• We compare results of the generated reports through quantitative evaluation matrices.• Researchers integrate convolutional neural network and recurrent neural network.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Amina Bolatkan
2. Kazuma Kobayashi
3. Jun Sese
4. Ryuji Hamamoto

In Molecular Biology

1. Syuzo Kaneko
2. Masaaki Komatsu
3. Ken Asada
4. Ken Takasawa
5. Amina Bolatkan
6. Jun Sese
7. Ryuji Hamamoto
8. Yusuke Nakamura

Citations in the corpus

• Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “Typical deep learning techniques used in medical imaging include convolutional neural networks for image classification [6], recurrent neural networks for reporting tasks [7], and a U-net for image segmentation [8].”