One-step abductive multi-target learning with diverse noisy samples and its application to tumour segmentation for breast cancer

Author:

Yang YongquanORCID,Li Fengling,Wei Yani,Chen Jie,Chen Ning,Alobaidi Mohammad H.,Bu Hong

Publisher

Elsevier BV

Reference52 articles.

1. Devil is in the edges: Learning semantic boundaries from noisy annotations;Acuna,2019

2. Image classification with deep learning in the presence of noisy labels: A survey;Algan;Knowledge-Based Systems,2021

3. Arazo, E., Ortego, D., Albert, P., O’Connor, N. E., & McGuinness, K. (2019). Unsupervised label noise modeling and loss correction. 36th International Conference on Machine Learning, ICML 2019.

4. SegNet: A deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Transactions on Pattern Analysis and Machine Intelligence,2017

5. Invasive region segmentation using pre-trained UNet and prognosis analysis of breast cancer based on tumor-stroma ratio;Bhattacharjee,2022

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