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15 results on '"Qu, Qixun"'

1. Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

Author

Zhu, Chuang, Liu, Shengjie, Yu, Zekuan, Xu, Feng, Aggarwal, Arpit, Corredor, Germán, Madabhushi, Anant, Qu, Qixun, Fan, Hongwei, Li, Fangda, Li, Yueheng, Guan, Xianchao, Zhang, Yongbing, Singh, Vivek Kumar, Akram, Farhan, Sarker, Md. Mostafa Kamal, Shi, Zhongyue, and Jin, Mulan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation., Comment: 12 pages, 12 figures, 2tables

Published
2023

3. Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

Author

Zhu, Chuang, primary, Liu, Shengjie, primary, Xu, Feng, primary, Yu, Zekuan, primary, Aggarwal, Arpit, primary, Corredor, Germán, primary, Madabhushi, Anant, primary, Qu, Qixun, primary, Fan, Hongwei, primary, Li, Fangda, primary, Li, Yueheng, primary, Guan, Xianchao, primary, Zhang, Yongbing, primary, Singh, Vivek Kumar, primary, Akram, Farhan, primary, Sarker, Md. Mostafa Kamal, primary, Shi, Zhongyue, primary, and Jin, Mulan, primary

Published
2023
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4. BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time Series

Author

Nascetti, Andrea, Yadav, Ritu, Brodt, Kiril, Qu, Qixun, Fan, Hongwei, Shendryk, Yuri, Shah, Isha, Chung, Christine, Nascetti, Andrea, Yadav, Ritu, Brodt, Kiril, Qu, Qixun, Fan, Hongwei, Shendryk, Yuri, Shah, Isha, and Chung, Christine

Abstract

Above Ground Biomass is an important variable as forests play a crucial role in mitigating climate change as they act as an efficient, natural and cost-effective carbon sink. Traditional field and airborne LiDAR measurements have been proven to provide reliable estimations of forest biomass. Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale. However, the full potential of dense multi-modal satellite time series data, in combination with modern Deep Learning (DL) approaches, has yet to be fully explored. The aim of the "BioMassters" data challenge and benchmark dataset is to investigate the potential of multi-modal satellite data (Sentinel-1 SAR and Sentinel-2 MSI) to estimate forest biomass at a large scale using the Finnish Forest Centre's open forest and nature airborne LiDAR data as a reference. The performance of the top three baseline models shows the potential of DL to produce accurate and higher-resolution biomass maps. The dataset and the code are available on the project website: https://nascetti-a.github.io/BioMasster/., QC 20240506

Published
2023

7. Predictive Classification System for Low Back Pain Based on Unsupervised Clustering

Author

Chun Jiang, Shi Yuanlu, Weibin Shi, Chang Jiang, Shen Linghao, Na Shen, Lixia Jin, Zhenzhou Feng, Zixian Chen, Mengying Jiang, Lishu Gu, Yuanwu Cao, Qu Qixun, and Xiaoxing Jiang

Subjects
medicine.medical_specialty, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, Low back pain, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Lumbar, medicine, Orthopedics and Sports Medicine, Surgery, 030212 general & internal medicine, Neurology (clinical), medicine.symptom, Unsupervised clustering, business, 030217 neurology & neurosurgery
Abstract

Study Design: Retrospective study. Objective: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. Method: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method. Results: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP ( P = .017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1. Conclusion: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment.

Published
2021
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8. Predictive Classification System for Low Back Pain Based on Unsupervised Clustering.

Author

Jin, Lixia, Jiang, Chang, Gu, Lishu, Jiang, Mengying, Shi, Yuanlu, Qu, Qixun, Shen, Na, Shi, Weibin, Cao, Yuanwu, Chen, Zixian, Jiang, Chun, Feng, Zhenzhou, Shen, Linghao, and Jiang, Xiaoxing

Subjects
LUMBAR pain, MAGNETIC resonance imaging, DISEASE incidence, FOLLOW-up studies (Medicine), MACHINE learning, DEGENERATION (Pathology)
Abstract

Study Design: Retrospective study. Objective: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. Method: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method. Results: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP (P =.017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1. Conclusion: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Supplemental Material, sj-docx-1-gsj-10.1177_21925682211001813 - Predictive Classification System for Low Back Pain Based on Unsupervised Clustering

Author

Jin, Lixia, Jiang, Chang, Gu, Lishu, Jiang, Mengying, Shi, Yuanlu, Qu, Qixun, Shen, Na, Shi, Weibin, Cao, Yuanwu, Chen, Zixian, Jiang, Chun, Feng, Zhenzhou, Shen, Linghao, and Jiang, Xiaoxing

Subjects
FOS: Clinical medicine, 110323 Surgery, 110604 Sports Medicine, FOS: Health sciences, 110904 Neurology and Neuromuscular Diseases, 110314 Orthopaedics
Abstract

Supplemental Material, sj-docx-1-gsj-10.1177_21925682211001813 for Predictive Classification System for Low Back Pain Based on Unsupervised Clustering by Lixia Jin, Chang Jiang, Lishu Gu, Mengying Jiang, Yuanlu Shi, Qixun Qu, Na Shen, Weibin Shi, Yuanwu Cao, Zixian Chen, Chun Jiang, Zhenzhou Feng, Linghao Shen and Xiaoxing Jiang in Global Spine Journal

Published
2021
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