Overview of ImageCLEF 2018 Medical Domain Visual Question Answering Task

By Sadid A. Hasan, Yuan Ling, Oladimeji Farri, Joey Liu, Henning Müller, Matthew P. Lungren. 2018

117 citations recorded by the source corpus.

How researchers see this paper

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

1. Erico Tjoa
2. Asma Ben Abacha
3. Sadid A. Hasan
4. Vivek Datla
5. Cuntai Guan
6. Joey Liu
7. Dina Demner-Fushman
8. Henning Müller

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “h variable. We can see that interpretability issues are not always clear-cut. Several researches on Visual Question Answering in the medical field have also been developed. The initiative by ImageCLEF [164], [165] appears to be at its center, though VQA itself has yet to gain more traction and succes”
• VQA-Med: Overview of the Medical Visual Question Answering Task at ImageCLEF 2019 (2019, methodology): “Inspired by the success of visual question answering in the general domain, we conducted a pilot task (VQA-Med 2018) in ImageCLEF 2018 to focus on visual question answering in the medical domain [9].”