We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions.
Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude.
no code implementations • 8 Dec 2021 • Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces.
In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence.
no code implementations • 22 Jun 2020 • Xiahai Zhuang, Jiahang Xu, Xinzhe Luo, Chen Chen, Cheng Ouyang, Daniel Rueckert, Victor M. Campello, Karim Lekadir, Sulaiman Vesal, Nishant Ravikumar, Yashu Liu, Gongning Luo, Jingkun Chen, Hongwei Li, Buntheng Ly, Maxime Sermesant, Holger Roth, Wentao Zhu, Jiexiang Wang, Xinghao Ding, Xinyue Wang, Sen yang, Lei LI
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
no code implementations • 26 Apr 2020 • Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu, Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt, Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara, Nicolo Savioli, Pablo Lamata, Jichao Zhao
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
In this study, we investigate the impact of various data augmentation algorithms, e. g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem.
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world.
In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality.
We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features.
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer.
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).
This underlines the need for an accurate and automatic approach to skin lesion segmentation.
We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge.
Early detection and segmentation of skin lesions are crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients.
Breast cancer is one of the leading causes of mortality in women.
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors.
In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers.
The proposed method achieved an average Dice coefficient of 0. 7808$\pm$0. 1729 and Jaccard index of 0. 6704$\pm$0. 2167.