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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

February 1, 2023

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Authors

Christy Y. Li

Duke University


Xiaodan Liang

Sun Yat-sen University


Zhiting Hu

Carnegie Mellon University


Eric P. Xing

Petuum Inc.


DOI:

10.1609/aaai.v33i01.33016666


Abstract:

Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase (KERP) approach which reconciles traditional knowledge- and retrieval-based methods with modern learning-based methods for accurate and robust medical report generation. Specifically, KERP decomposes medical report generation into explicit medical abnormality graph learning and subsequent natural language modeling. KERP first employs an Encode module that transforms visual features into a structured abnormality graph by incorporating prior medical knowledge; then a Retrieve module that retrieves text templates based on the detected abnormalities; and lastly, a Paraphrase module that rewrites the templates according to specific cases. The core of KERP is a proposed generic implementation unit—Graph Transformer (GTR) that dynamically transforms high-level semantics between graph-structured data of multiple domains such as knowledge graphs, images and sequences. Experiments show that the proposed approach generates structured and robust reports supported with accurate abnormality description and explainable attentive regions, achieving the state-of-the-art results on two medical report benchmarks, with the best medical abnormality and disease classification accuracy and improved human evaluation performance.

Topics: AAAI

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HOW TO CITE:

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation Proceedings of the AAAI Conference on Artificial Intelligence (2019) 6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation AAAI 2019, 6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing (2019). Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing. Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing. 2019. Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. "Proceedings of the AAAI Conference on Artificial Intelligence". 6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing. (2019) "Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation", Proceedings of the AAAI Conference on Artificial Intelligence, p.6666-6673

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing, "Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation", AAAI, p.6666-6673, 2019.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing. "Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing. "Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 6666-6673.

Christy Y. Li||Xiaodan Liang||Zhiting Hu||Eric P. Xing. Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. AAAI[Internet]. 2019[cited 2023]; 6666-6673.


ISSN: 2374-3468


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