Search Results for author: Riadh Kobbi

Found 4 papers, 2 papers with code

Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy

no code implementations27 Jan 2024 Julio Silva-Rodriguez, Jihed Chelbi, Waziha Kabir, Hadi Chakor, Jose Dolz, Ismail Ben Ayed, Riadh Kobbi

In this work, we explore the potential of using FLAIR features as starting point for fundus image classification, and we compare its performance with regard to Imagenet initialization on two popular transfer learning methods: Linear Probing (LP) and Fine-Tuning (FP).

Image Classification Medical Image Classification +1

A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision

1 code implementation15 Aug 2023 Julio Silva-Rodriguez, Hadi Chakor, Riadh Kobbi, Jose Dolz, Ismail Ben Ayed

Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities.

Descriptive Language Modelling

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

Do We Really Need Dice? The Hidden Region-Size Biases of Segmentation Losses

1 code implementation18 Apr 2021 Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed

Most segmentation losses are arguably variants of the Cross-Entropy (CE) or Dice losses.

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