The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

1 code implementation • 5 Jul 2023 • Nicholas Heller, Fabian Isensee, Dasha Trofimova, Resha Tejpaul, Zhongchen Zhao, Huai Chen, Lisheng Wang, Alex Golts, Daniel Khapun, Daniel Shats, Yoel Shoshan, Flora Gilboa-Solomon, Yasmeen George, Xi Yang, Jianpeng Zhang, Jing Zhang, Yong Xia, Mengran Wu, Zhiyang Liu, Ed Walczak, Sean McSweeney, Ranveer Vasdev, Chris Hornung, Rafat Solaiman, Jamee Schoephoerster, Bailey Abernathy, David Wu, Safa Abdulkadir, Ben Byun, Justice Spriggs, Griffin Struyk, Alexandra Austin, Ben Simpson, Michael Hagstrom, Sierra Virnig, John French, Nitin Venkatesh, Sarah Chan, Keenan Moore, Anna Jacobsen, Susan Austin, Mark Austin, Subodh Regmi, Nikolaos Papanikolopoulos, Christopher Weight

Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.

Tumor Segmentation

3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations

no code implementations • 7 Feb 2023 • Huai Chen, Xiuying Wang, Lisheng Wang

Accordingly, the 3D region discrimination loss is firstly proposed to learn the discriminative representation measuring voxel-wise similarities and cluster semantically consistent voxels to form the candidate 3D vascular segmentation in unlabeled images; secondly, based on the similarity of the tree-shaped morphology between 2D and 3D vessels, the Crop-and-Overlap strategy is presented to generate reference masks from 2D structure-agnostic vessel annotations, which are fit for varied vascular structures, and the adversarial loss is introduced to guide the tree-shaped morphology of 3D vessels; thirdly, the temporal consistency loss is proposed to foster the training stability and keep the model updated smoothly.

Unsupervised Local Discrimination for Medical Images

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

To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.

Contrastive Learning Lesion Segmentation +1

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.

Clustering 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.