2 code implementations • 4 Dec 2023 • Mohammed Baharoon, Waseem Qureshi, Jiahong Ouyang, Yanwu Xu, Abdulrhman Aljouie, Wei Peng
To measure the effectiveness and generalizability of DINOv2's feature representations, we analyze the model across medical image analysis tasks including disease classification and organ segmentation on both 2D and 3D images, and under different settings like kNN, few-shot learning, linear-probing, end-to-end fine-tuning, and parameter-efficient fine-tuning.
2 code implementations • 2 Oct 2023 • Mohammed Baharoon, Hessa Almatar, Reema Alduhayan, Tariq Aldebasi, Badr Alahmadi, Yahya Bokhari, Mohammed Alawad, Ahmed Almazroa, Abdulrhman Aljouie
Our MMDL system uses RETFound, a foundation model pre-trained on 1. 6 million retinal images, for the fundus path and a fully connected neural network for the age and gender path.