Self-Supervised Versus Supervised Training for Segmentation of Organoid Images

no code implementations • 19 Nov 2023 • Asmaa Haja, Eric Brouwer, Lambert Schomaker

When trained on only 114 images for the main task, the self-supervised learning approach outperforms the supervised method achieving an F1-score of 0. 85, with higher stability, in contrast to an F1=0. 78 scored by the supervised method.

Image Segmentation Self-Supervised Learning +2

A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification

no code implementations • 6 Apr 2021 • Asmaa Haja, Lambert R. B. Schomaker

With this intention we used Mask R-CNN to automatically segment and label a large amount of yeast cell data, and YOLOv4 to automatically detect and classify individual yeast cell compartments from these images.

General Classification