# AI assisted method for efficiently generating breast ultrasound screening reports

arXiv: https://arxiv.org/abs/2107.13431 · HTML: https://mlsanity.com/paper/2107.13431/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [Chest X-ray Report Generation through Fine-Grained Label Learning](https://mlsanity.com/paper/2007.13831/) (2020)
- [Producing radiologist-quality reports for interpretable artificial intelligence](https://mlsanity.com/paper/1806.00340/) (2018)

## Built on by (most cited first, 2 of 2)

- [MMSummary: Multimodal Summary Generation for Fetal Ultrasound Video](https://mlsanity.com/paper/2408.03761/) (2024)
- [BUSTR: Descriptor-Aware Vision-Language Learning for Breast Ultrasound Report Generation](https://mlsanity.com/paper/2511.20956/) (2025)

## Abstract (arXiv)

Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report which is time-consuming and laborious, and it is easy to miss and miswrite. Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing. Methods: AI was used to efficiently generate personalized breast ultrasound screening preliminary reports, especially for benign and normal cases which account for the majority. Based on the preliminary AI report, doctors then make simple adjustments or corrections to quickly generate the final report. The approach has been trained and tested using a database of 4809 breast tumor instances. Results: Experimental results indicate that this pipeline improves doctors' work efficiency by up to 90%, which greatly reduces repetitive work. Conclusion: Personalized report generation is more widely recognized by doctors in clinical practice compared with non-intelligent reports based on fixed templates or containing options to fill in the blanks.