Fetal Brain Tissue Annotation and Segmentation Challenge Results

no code implementations • 20 Apr 2022 • Kelly Payette, Hongwei Li, Priscille de Dumast, Roxane Licandro, Hui Ji, Md Mahfuzur Rahman Siddiquee, Daguang Xu, Andriy Myronenko, Hao liu, Yuchen Pei, Lisheng Wang, Ying Peng, Juanying Xie, Huiquan Zhang, Guiming Dong, Hao Fu, Guotai Wang, ZunHyan Rieu, Donghyeon Kim, Hyun Gi Kim, Davood Karimi, Ali Gholipour, Helena R. Torres, Bruno Oliveira, João L. Vilaça, Yang Lin, Netanell Avisdris, Ori Ben-Zvi, Dafna Ben Bashat, Lucas Fidon, Michael Aertsen, Tom Vercauteren, Daniel Sobotka, Georg Langs, Mireia Alenyà, Maria Inmaculada Villanueva, Oscar Camara, Bella Specktor Fadida, Leo Joskowicz, Liao Weibin, Lv Yi, Li Xuesong, Moona Mazher, Abdul Qayyum, Domenec Puig, Hamza Kebiri, Zelin Zhang, Xinyi Xu, Dan Wu, Kuanlun Liao, Yixuan Wu, Jintai Chen, Yunzhi Xu, Li Zhao, Lana Vasung, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab

Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.

Ensemble Learning

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.

3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

2 code implementations • 27 Jun 2018 • Yi-Jie Huang, Qi Dou, Zi-Xian Wang, Li-Zhi Liu, Ying Jin, Chao-Feng Li, Lisheng Wang, Hao Chen, Rui-Hua Xu

With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field.

Multi-Task Learning Tumor Segmentation

Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images

no code implementations • 27 Sep 2017 • Heran Yang, Jian Sun, Huibin Li, Lisheng Wang, Zongben Xu

There are two major challenges in this category of methods, i. e., atlas selection and label fusion.

Left Ventricle Segmentation Semantic Segmentation