Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation

no code implementations • 2 Aug 2023 • Ziyi Huang, Hongshan Liu, Haofeng Zhang, Xueshen Li, Haozhe Liu, Fuyong Xing, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan

One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.

Image Segmentation Medical Image Segmentation +4

Cardiac Adipose Tissue Segmentation via Image-Level Annotations

no code implementations • 9 Jun 2022 • Ziyi Huang, Yu Gan, Theresa Lye, Yanchen Liu, Haofeng Zhang, Andrew Laine, Elsa Angelini, Christine Hendon

To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates.

Segmentation Weakly-supervised Learning

Co-Seg: An Image Segmentation Framework Against Label Corruption

no code implementations • 31 Jan 2021 • Ziyi Huang, Haofeng Zhang, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan

Supervised deep learning performance is heavily tied to the availability of high-quality labels for training.

Image Segmentation Segmentation +1