ENHANCING AI MODELS FOR RARE THYROID CANCER DIAGNOSIS USING DEEP LEARNING AND QUASI-PARETO IMPROVEMENT

Research output: Contribution to conference › Paper › peer-review

Original language	English
DOIs	https://doi.org/10.1016/j.ultrasmedbio.2025.11.371
Publication status	Published - 13 Dec 2025
Externally published	Yes
Event	The 20th Congress of World Federation for Ultrasound in Medicine and Biology - , Japan Duration: 30 May 2025 → 2 Jun 2025

Conference	The 20th Congress of World Federation for Ultrasound in Medicine and Biology
Country/Territory	Japan
Period	30/05/25 → 2/06/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

@conference{31407afd2a3748e1bf29dbe883356865,

title = "ENHANCING AI MODELS FOR RARE THYROID CANCER DIAGNOSIS USING DEEP LEARNING AND QUASI-PARETO IMPROVEMENT",

author = "Yincheng Gao and Peiqi Li and Bojie Liu and Lehang Guo",

year = "2025",

month = dec,

day = "13",

doi = "10.1016/j.ultrasmedbio.2025.11.371",

language = "English",

note = "The 20th Congress of World Federation for Ultrasound in Medicine and Biology ; Conference date: 30-05-2025 Through 02-06-2025",

}

TY - CONF

T1 - ENHANCING AI MODELS FOR RARE THYROID CANCER DIAGNOSIS USING DEEP LEARNING AND QUASI-PARETO IMPROVEMENT

AU - Gao, Yincheng

AU - Li, Peiqi

AU - Liu, Bojie

AU - Guo, Lehang

PY - 2025/12/13

Y1 - 2025/12/13

U2 - 10.1016/j.ultrasmedbio.2025.11.371

DO - 10.1016/j.ultrasmedbio.2025.11.371

M3 - Paper

T2 - The 20th Congress of World Federation for Ultrasound in Medicine and Biology

Y2 - 30 May 2025 through 2 June 2025

ER -