Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors

no code implementations • 13 Nov 2023 • Marc Aubreville, Zhaoya Pan, Matti Sievert, Jonas Ammeling, Jonathan Ganz, Nicolai Oetter, Florian Stelzle, Ann-Kathrin Frenken, Katharina Breininger, Miguel Goncalves

This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections.

Few-Shot Learning

ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation

no code implementations • 8 Jun 2022 • Mingxuan Gu, Sulaiman Vesal, Mareike Thies, Zhaoya Pan, Fabian Wagner, Mirabela Rusu, Andreas Maier, Ronak Kosti

Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude.

Contrastive Learning Image Segmentation +4

What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation

no code implementations • 6 Nov 2019 • Weilin Fu, Katharina Breininger, Zhaoya Pan, Andreas Maier

Results show that for retinal vessel segmentation on DRIVE database, U-Net does not degenerate until surprisingly acute conditions: one level, one filter in convolutional layers, and one training sample.

Retinal Vessel Segmentation Segmentation