# Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation

arXiv: https://arxiv.org/abs/2203.06458 · HTML: https://mlsanity.com/paper/2203.06458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation](https://mlsanity.com/paper/1805.08298/) (2018)
- [A Hierarchical Approach for Generating Descriptive Image Paragraphs](https://mlsanity.com/paper/1611.06607/) (2016)
- [MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network](https://mlsanity.com/paper/1707.02485/) (2017)

## Abstract (arXiv)

Echocardiography is widely used to clinical practice for diagnosis and treatment, e.g., on the common congenital heart defects. The traditional manual manipulation is error-prone due to the staff shortage, excess workload, and less experience, leading to the urgent requirement of an automated computer-aided reporting system to lighten the workload of ultrasonologists considerably and assist them in decision making. Despite some recent successful attempts in automatical medical report generation, they are trapped in the ultrasound report generation, which involves unstructured-view images and topic-related descriptions. To this end, we investigate the task of the unstructured-view topic-related ultrasound report generation, and propose a novel factored attention and embedding model (termed FAE-Gen). The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which 1) capture the homogeneous and heterogeneous morphological characteristic across different views, and 2) generate the descriptions with different syntactic patterns and different emphatic contents for different topics. Experimental evaluations are conducted on a to-be-released large-scale clinical cardiovascular ultrasound dataset (CardUltData). Both quantitative comparisons and qualitative analysis demonstrate the effectiveness and the superiority of FAE-Gen over seven commonly-used metrics.