Mou-Cheng Xu | DeepAI

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∙ 05/02/2023

Expectation Maximization Pseudo Labelling for Segmentation with Limited Annotations

We study pseudo labelling and its generalisation for semi-supervised seg...

0 Mou-Cheng Xu, et al. ∙

∙ 10/17/2022

Deformably-Scaled Transposed Convolution

Transposed convolution is crucial for generating high-resolution outputs...

8 Stefano B. Blumberg, et al. ∙

∙ 08/30/2022

Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis

Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) ...

9 Ashkan Pakzad, et al. ∙

∙ 08/08/2022

Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation

This paper concerns pseudo labelling in segmentation. Our contribution i...

47 Mou-Cheng Xu, et al. ∙

∙ 03/19/2022

Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation

We propose MisMatch, a novel consistency-driven semi-supervised segmenta...

0 Mou-Cheng Xu, et al. ∙

∙ 10/23/2021

MisMatch: Learning to Change Predictive Confidences with Attention for Consistency-Based, Semi-Supervised Medical Image Segmentation

The lack of labels is one of the fundamental constraints in deep learnin...

0 Mou-Cheng Xu, et al. ∙

∙ 07/31/2020

Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Recent years have seen increasing use of supervised learning methods for...

0 Le Zhang, et al. ∙

∙ 07/29/2020

Learning To Pay Attention To Mistakes

As evidenced in visual results in <cit.><cit.><cit.><cit.><cit.>, the pe...

0 Mou-Cheng Xu, et al. ∙

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