LLM์์ MOE ๊ธฐ๋ฒ์ ์ ์ฉํ์ฌ Medical Domain์์ Task๋ฅผ ์ํํ๋ Reference๋ฅผ ์ฐพ์๋ณด๋ฉด์ ๋ ผ๋ฌธ์ ์ฝ์ ๋ด์ฉ์ ์ ๋ฆฌํด ๋ณด๋ ค๊ณ ํฉ๋๋ค.๋ ผ๋ฌธ ์๋ฌธ ์ฌ์ดํธ When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical ApplicationsThe recent surge in Large Language Models (LLMs) has garnered significant attention across numerous fields. Fine-tuning is often required to fit general LLMs for a specific domain, like the web-based healthcare sy..
Large Vision Language Model์ Medical ๋๋ฉ์ธ์ ์ฐ๊ด๋ ๋ด์ฉ์ ๊ณต๋ถํด๋ณด๋ค๊ฐ ๋ ผ๋ฌธ์ ์ฝ์ด์ ํ๋ฒ ์ ๋ฆฌํด๋ณด๊ฒ ์ต๋๋ค.๋ ผ๋ฌธ ์๋ฌธ ์ฌ์ดํธ Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question AnsweringLarge Vision-Language Models (LVLMs) have achieved significant success in recent years, and they have been extended to the medical domain. Although demonstrating satisfactory performance on medical Visual Questio..
๋ ผ๋ฌธ์ ๊ณ์ ์ฝ์ด์ผ์ง ์ฝ์ด์ผ์ง ์๊ฐํ๋ค๊ฐ.. ์ฉ๊ธฐ๋ฅผ ๋ด์ด์ ํ๋ฒ ์ฝ์ด๋ณธ ๋ด์ฉ์ ์ ๋ฆฌํด๋ณด๊ฒ ์ต๋๋ค. VGGNet Paper (2014)VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION.๋ ผ๋ฌธ ์ฌ์ดํธ ๋งํฌ๋ ์๋์ ๋จ๊ฒจ๋๊ฒ ์ต๋๋ค. ๊ทธ๋ฌ๋ฉด ํ๋ฒ ์ฐจ๊ทผ์ฐจ๊ทผ ๋ฆฌ๋ทฐํด ๋ณด๊ฒ ์ต๋๋ค. Very Deep Convolutional Networks for Large-Scale Image RecognitionIn this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main ..