no code implementations • 18 Jan 2024 • Hang Zhang, Xiang Chen, Rongguang Wang, Renjiu Hu, Dongdong Liu, Gaolei Li
In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures.
no code implementations • 17 Jan 2024 • Junhao Wen, Mathilde Antoniades, Zhijian Yang, Gyujoon Hwang, Ioanna Skampardoni, Rongguang Wang, Christos Davatzikos
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders.
1 code implementation • 27 Nov 2023 • Xiang Chen, Min Liu, Rongguang Wang, Renjiu Hu, Dongdong Liu, Gaolei Li, Hang Zhang
Medical images are often characterized by their structured anatomical representations and spatially inhomogeneous contrasts.
Ranked #2 on Image Registration on Unpaired-abdomen-CT (using extra training data)
no code implementations • 6 Aug 2023 • Rongguang Wang, Guray Erus, Pratik Chaudhari, Christos Davatzikos
In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set.
1 code implementation • 5 Jun 2023 • Hang Zhang, Renjiu Hu, Xiang Chen, Rongguang Wang, Jinwei Zhang, Jiahao Li
Specifically, the network incorporating DAGrid has realized a 70. 8% reduction in network parameter size and a 96. 8% decrease in FLOPs, while concurrently improving the Dice score for skin lesion segmentation by 1. 0% compared to state-of-the-art transformers.
1 code implementation • 15 Mar 2023 • Hang Zhang, Rongguang Wang, Renjiu Hu, Jinwei Zhang, Jiahao Li
Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps.
no code implementations • 19 Jan 2023 • Hang Zhang, Rongguang Wang, Jinwei Zhang, Dongdong Liu, Chao Li, Jiahao Li
Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by injecting proper priors into neural networks.
no code implementations • 14 Jun 2022 • Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Sai Spandana Chintapalli, Ioanna Skampardoni, Lasya P. Sreepada, Dushyant Sahoo, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen, Christos Davatzikos
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields.
no code implementations • 26 May 2022 • Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols.
1 code implementation • 12 Jun 2021 • Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Heterogeneity in medical data, e. g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize well.
no code implementations • 23 Mar 2021 • Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
We can also tackle situations where we do not have access to ground-truth labels on target data; we show how one can use auxiliary tasks for adaptation; these tasks employ covariates such as age, gender and race which are easy to obtain but nevertheless correlated to the main task.
1 code implementation • 6 Mar 2021 • Hang Zhang, Rongguang Wang, Jinwei Zhang, Chao Li, Gufeng Yang, Pascal Spincemaille, Thanh Nguyen, Yi Wang
We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution.
no code implementations • 29 Sep 2020 • Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Susan A. Gauthier, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for training deep learning based segmentation models.
no code implementations • 13 Sep 2020 • Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture global contextual information for performance enhancement.