Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation

08/21/2019
by   Jiahua Dong, et al.
0

Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse lesions regions; 2) they ignore the strong probabilistic dependencies between target lesions dataset (e.g., enteroscopy images) and well-to-annotated source diseases dataset (e.g., gastroscope images). To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task. More specifically, a pseudo label generator is proposed to leverage seed information to generate highly-confident pseudo pixel labels by incorporating class balance and super-pixel spatial prior. It can iteratively include more hard-to-transfer samples from weakly-labeled target dataset into training set. Afterwards, dynamically searched feature centroids for same class among different datasets are aligned by accumulating previously-learned features. Meanwhile, adversarial learning is also employed in this paper, to narrow the gap between the lesions among different datasets in output space. Finally, we build a new medical endoscopic dataset with 3659 images collected from more than 1100 volunteers. Extensive experiments on our collected dataset and several benchmark datasets validate the effectiveness of our model.

READ FULL TEXT

page 3

page 7

research
12/08/2020

Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation

Weakly-supervised learning has attracted growing research attention on m...
research
04/24/2020

What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation

Unsupervised domain adaptation has attracted growing research attention ...
research
10/14/2021

Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

Though image-level weakly supervised semantic segmentation (WSSS) has ac...
research
10/20/2021

Simpler Does It: Generating Semantic Labels with Objectness Guidance

Existing weakly or semi-supervised semantic segmentation methods utilize...
research
09/18/2023

Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency

Multi-organ segmentation in abdominal Computed Tomography (CT) images is...
research
03/20/2022

Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions

The medical datasets are usually faced with the problem of scarcity and ...
research
12/20/2022

Weakly supervised training of universal visual concepts for multi-domain semantic segmentation

Deep supervised models have an unprecedented capacity to absorb large qu...

Please sign up or login with your details

Forgot password? Click here to reset