Unsupervised Local Discrimination for Medical Images

1 code implementation • 21 Aug 2021 • Huai Chen, Renzhen Wang, Jieyu Li, Jianhao Bai, Qing Peng, Deyu Meng, Lisheng Wang

Following the fact that images of the same body region should share similar anatomical structures, and pixels of the same structure should have similar semantic patterns, we design a neural network to construct a local discriminative embedding space where pixels with similar contexts are clustered and dissimilar pixels are dispersed.

Contrastive Learning Representation Learning

Unsupervised Learning of Local Discriminative Representation for Medical Images

1 code implementation • 17 Dec 2020 • Huai Chen, Jieyu Li, Renzhen Wang, YiJie Huang, Fanrui Meng, Deyu Meng, Qing Peng, Lisheng Wang

However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.

Representation Learning

COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19

no code implementations • 10 Dec 2020 • Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong, Yong Yin, Lisheng Wang, Xiuying Wang

In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.

COVID-19 Diagnosis Transfer Learning

MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

no code implementations • 25 Dec 2018 • Huai Chen, Yuxiao Qi, Yong Yin, Tengxiang Li, Xiaoqing Liu, Xiuli Li, Guanzhong Gong, Lisheng Wang

Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC.