Search Results for author: Qinquan Gao

Found 7 papers, 4 papers with code

A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification

no code implementations24 Nov 2023 Xiangyu Xiong, Yue Sun, Xiaohong Liu, Chan-Tong Lam, Tong Tong, Hao Chen, Qinquan Gao, Wei Ke, Tao Tan

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.

Data Augmentation Generative Adversarial Network +2

Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

1 code implementation17 Nov 2023 Tao Wang, Yuanbin Chen, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Bizhe Bai, Tao Tan, Min Du, Qinquan Gao, Tong Tong

Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation.

Image Segmentation Pseudo Label +3

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

1 code implementation29 Jun 2023 Tao Wang, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Tao Tan, Min Du, Qinquan Gao, Tong Tong

To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks.

Active Learning Image Classification +5

Image Super-Resolution Using Dense Skip Connections

no code implementations ICCV 2017 Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao

In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network.

Computational Efficiency Image Super-Resolution

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