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20182022
most citedDeep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019

8 citations · 13 across the 5 of their papers we have counts for

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cs.CV20221 cited

Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning

Luyi Han, Yunzhi Huang, Tao Tan +1

Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed…

cs.CV2019

Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets

Jie Xing, Zheren Li, Biyuan Wang +6

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the b…

cs.CV2018

Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study

Zhang Li, Zheyu Hu, Jiaolong Xu +10

Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform be…

cs.CV2018

Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning

Tao Tan, Zhang Li, Haixia Liu +15

Bronchoscopy inspection as a follow-up procedure from the radiological imaging plays a key role in lung disease diagnosis and determining treatment plans for the patients. Doctors…

cs.CV20183 cited

Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

Dongsheng Jiang, Weiqiang Dou, Luc Vosters +3

The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retriev…