Co-Authors
This is a "connection" page, showing publications co-authored by Yuchen Qiu and Theresa Thai.
Connection Strength
1.754
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Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients. Comput Biol Med. 2024 Apr; 172:108240.
Score: 0.235
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Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy. Bioengineering (Basel). 2023 Nov 20; 10(11).
Score: 0.230
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Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients. ArXiv. 2023 Sep 13.
Score: 0.228
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Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022 07; 79:102444.
Score: 0.206
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Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients. Comput Methods Programs Biomed. 2020 Dec; 197:105759.
Score: 0.185
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Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker. Phys Med Biol. 2018 08 06; 63(15):155020.
Score: 0.160
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Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks. Ann Biomed Eng. 2018 Dec; 46(12):1988-1999.
Score: 0.159
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Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy. Acad Radiol. 2017 10; 24(10):1233-1239.
Score: 0.147
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Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis. Acta Radiol. 2016 Sep; 57(9):1149-55.
Score: 0.133
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A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images. Comput Methods Programs Biomed. 2017 Jun; 144:97-104.
Score: 0.036
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Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome. BMC Med Imaging. 2016 08 31; 16(1):52.
Score: 0.035