Abstract
This paper describes the methods used for the NAACL 2024 workshop MEDIQA-M3G shared task for generating medical answers from image and query data for skin diseases. MedVInT-Decoder, LLaVA, and LLaVA-Med are chosen as base models. Finetuned with the task dataset on the dermatological domain, MedVInT-Decoder achieved a BLEU score of 3.82 during competition, while LLaVA and LLaVA-Med reached 6.98 and 4.62 afterward, respectively.- Anthology ID:
- 2024.clinicalnlp-1.30
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 334–338
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.30
- DOI:
- Cite (ACL):
- Hyeonjin Kim, Min Kim, Jae Jang, KiYoon Yoo, and Nojun Kwak. 2024. TEAM MIPAL at MEDIQA-M3G 2024: Large VQA Models for Dermatological Diagnosis. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 334–338, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- TEAM MIPAL at MEDIQA-M3G 2024: Large VQA Models for Dermatological Diagnosis (Kim et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.30.pdf