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Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation

This repo contains the source code and dataset for our accepted ICCV 2019 paper:

Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation
2019 IEEE international Conference on Computer Vision (ICCV 2019)

Overview of Lesions Transfer Model


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, dynamicallysearched 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.


Datasets and source code will be coming soon ...


  • Python 3.6
  • Pytorch 4.0


If you find this project useful for your research, please kindly cite our paper:

author = {Dong, Jiahua and Cong, Yang and Sun, Gan and Hou, Dongdong},
title = {Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}


If you have any questions, please feel free to contact Jiahua Dong or Yang Cong.


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