Automated Knee X-ray Report Generation
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Abstract (arXiv)
Gathering manually annotated images for the purpose of training a predictive model is far more challenging in the medical domain than for natural images as it requires the expertise of qualified radiologists. We therefore propose to take advantage of past radiological exams (specifically, knee X-ray examinations) and formulate a framework capable of learning the correspondence between the images and reports, and hence be capable of generating diagnostic reports for a given X-ray examination consisting of an arbitrary number of image views. We demonstrate how aggregating the image features of individual exams and using them as conditional inputs when training a language generation model results in auto-generated exam reports that correlate well with radiologist-generated reports.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 22 May 2021.
Builds on
12 of 27 references.
References · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2014Going Deeper with Convolutionscited by 4,241
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
- 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 968
- 2014Show and Tell: A Neural Image Caption Generatorcited by 879
- 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 484
- 2014Unifying Visual-Semantic Embeddings with Multimodal Neural Language Modelscited by 198
- 2016Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotationcited by 59
- 2017MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Networkcited by 56
- 2017TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic Referencescited by 11
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Citing papers per year: 2022: 1.
1 paper.