Search

Your search keyword '"Yang, Hao-yu"' showing total 291 results
Start Over Author "Yang, Hao-yu"
291 results on '"Yang, Hao-yu"'

4. Ultrasonography Uterus and Fetus Segmentation with Constrained Spatial-Temporal Memory FCN

Author

Kong, Bin, Wang, Xin, Lu, Yi, Yang, Hao-Yu, Cao, Kunlin, Song, Qi, Yin, Youbing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Guang, editor, Aviles-Rivero, Angelica, editor, Roberts, Michael, editor, and Schönlieb, Carola-Bibiane, editor

Published
2022
Full Text
View/download PDF

5. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

14. Leukemic B-Lymphoblast Cell Detection with Monte Carlo Dropout Ensemble Models

Author

Yang, Hao-Yu, Staib, Lawrence H., Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Gupta, Anubha, editor, and Gupta, Ritu, editor

Published
2019
Full Text
View/download PDF

15. Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training

Author

Yang, Hao-Yu, Yang, Junlin, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
Full Text
View/download PDF

16. Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation

Author

Yang, Hao-Yu, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Keyvan, Farahani, editor, Reyes, Mauricio, editor, and van Walsum, Theo, editor

Published
2019
Full Text
View/download PDF

19. Protein disulfide isomerase a4 promotes lung cancer development via the Stat3 pathway in stromal cells

Author

Tzung‐Yan Chen, Chun‐Yen Yang, Meng‐Ting Yang, Tien‐Fen Kuo, Cicero Lee‐Tian Chang, Chih‐Li Chen, Tsung‐Han Lee, Greta Yang, Wen‐Chin Yang, Ching‐Feng Chiu, and Alex Yang‐Hao Yu

Subjects
cancer stroma, lung cancer, Pdia4, Stat3, Medicine (General), R5-920
Abstract

Abstract Background Protein disulfide isomerases a4 (Pdia4) is known to be involved in cancer development. Our previous publication showed that Pdia4 positively promotes cancer development via its inhibition of procaspase‐dependent apoptosis in cancer cells. However, nothing is known about its role in the cancer microenvironment. Results Here, we first found that Pdia4 expression in lung cancer was negatively correlated with patient survival. Next, we investigated the impact of host Pdia4 in stromal cells during cancer development. We showed that Pdia4 was expressed at a low level in stromal cells, and this expression was up‐regulated akin to its expression in cancer cells. This up‐regulation was stimulated by tumour cell‐derived stimuli. Genetics studies in tumour‐bearing wild‐type and Pdia4–/– mice showed that host Pdia4 promoted lung cancer development in the mice via cancer stroma. This promotion was abolished in Rag1–/– mice which lacked T and B cells. This promotion could be restored once T and B cells were added back to Rag1–/– mice. In addition, host Pdia4 positively regulated the number and immunosuppressive function of stromal cells. Mechanistic studies showed that host Pdia4 positively controlled the Stat3/Vegf pathway in T and B lymphocytes via its stabilization of activated Stat3 in a Thioredoxin‐like domain (CGHC)‐dependent manner. Conclusions These findings identify Pdia4 as a possible target for intervention in cancer stroma, suggesting that targeting Pdia4 in cancer stroma is a promising anti‐cancer approach.

Published
2022
Full Text
View/download PDF

21. Epidermal growth factor receptors siRNA-conjugated collagen modified gold nanoparticles for targeted imaging and therapy of lung cancer

Author

Alex Yang-Hao Yu, Ru-Huei Fu, Shan-hui Hsu, Chen-Feng Chiu, Wei-Hsiang Fang, Chun-An Yeh, Cheng-Ming Tang, Hsien-Hsu Hsieh, and Huey-Shan Hung

Subjects
Epidermal growth factor receptor, Gold nanoparticle (AuNP), Small-interfering RNA (siRNA), Lung cancer, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Epidermal growth factor receptor (EGFR), a critical factor promotes lung cancer cell proliferation and survival. Knockdown of EGFR expression thus promise beating lung cancer clinically. Functionalized gold nanoparticles may serve an effective vehicle carrying theranostic bio-active materials. Herein, physically gold nanoparticles were fabricated with biocompatible collagen to improve siRNA loading capacity carrying EGFR siRNA to treat lung cancer. Physic-chemical properties were comprehensively characterized for the collagen gold nanoparticle (C–Au), and with EGFR siRNA conjugation, C–Au-EGFRsi namely. Issues of biocompatibility were addressed. Interestingly, C–Au appeared more biocompatible to normal airway epithelial cells (BEAS-2B) than to cancer cells (A549) in terms of ROS production, cell cycle behavior, and cell growth influence. The C–Au demonstrated comparable or even more efficient, compared with lipofetamine, in carrying siRNA to knockdown EGFR of A549 cells. Endocytosis mediated cell entry for the collagen gold nanoparticles, and endosome-lysosomal pathway involved transporting and metabolizing these nanoparticles. In xenograft mice model, substantial tumor suppression effects were observed treating with C–Au-EGFRsi, in which tumor weight reduced 30% for lipofetamine carrier, and down to 70% for C–Au carrier. Particularly, overall survival rate was improved for both treatment groups with lipofetamine and C–Au carrier, respectively.

Published
2021
Full Text
View/download PDF

35. Supplementary Figure 3 from miR326 Maturation Is Crucial for VEGF-C–Driven Cortactin Expression and Esophageal Cancer Progression

Author

Jen-Liang Su, Nan-Yung Hsu, Shine-Gwo Shiah, Nai-Wen Hsu, Mien-Chie Hung, Yang-Hao Yu, Yi-Wen Chang, Michael Hsiao, Ching-Yao Yang, Ching-Feng Chiu, Jean Chiou, Pai-Sheng Chen, and Chih-Chen Hong

Abstract

Supplementary Figure 3. The mRNA expression of CTTN in rhVEGF-C-treated TE-1 cells. TE-1 cells were treated with a various doses of rhVEGF-C (0, 25, 50, 100 and 200 ng/mL) for 24 hours and isolated total RNA to measure the expression level of CTTN by RT-PCR. GAPDH was used as an internal control.

Published
2023
Full Text
View/download PDF

36. Supplementary Figure 1 from miR326 Maturation Is Crucial for VEGF-C–Driven Cortactin Expression and Esophageal Cancer Progression

Author

Jen-Liang Su, Nan-Yung Hsu, Shine-Gwo Shiah, Nai-Wen Hsu, Mien-Chie Hung, Yang-Hao Yu, Yi-Wen Chang, Michael Hsiao, Ching-Yao Yang, Ching-Feng Chiu, Jean Chiou, Pai-Sheng Chen, and Chih-Chen Hong

Abstract

Supplementary Figure 1. The mRNA expression of Nrp1 and Nrp2 in ESCC cell lines. RT-PCR was showed the expression level of Nrp1 and Nrp2 in TE1, TE12, TE3, TE7, and TE8 cells. GAPDH was used as an internal control.

Published
2023
Full Text
View/download PDF

38. Supplementary Figure 4 from miR326 Maturation Is Crucial for VEGF-C–Driven Cortactin Expression and Esophageal Cancer Progression

Author

Jen-Liang Su, Nan-Yung Hsu, Shine-Gwo Shiah, Nai-Wen Hsu, Mien-Chie Hung, Yang-Hao Yu, Yi-Wen Chang, Michael Hsiao, Ching-Yao Yang, Ching-Feng Chiu, Jean Chiou, Pai-Sheng Chen, and Chih-Chen Hong

Abstract

Supplementary Figure 4. The mRNA expression of miR-326 in TE-8 cells with knockdown of VEGFR-3. TE-8 cells were transiently transfected shVEGFR-3#1, #2 or shCtrl for 48 hours and isolated total RNA to assay. A, qRT-PCR was used to quantify the expression level of miR-326 in those indicated cells. B, RT-PCR was showed the expression level of VEGFR-3 in those indicated cells. GAPDH was used as an internal control. The quantities values were evaluated as percentage compared with TE-8/shCtrl cells. The columns are the mean values from three independent experiments. Bars indicate means {plus minus} SE.

Published
2023
Full Text
View/download PDF

39. Supplementary Figure 5 from miR326 Maturation Is Crucial for VEGF-C–Driven Cortactin Expression and Esophageal Cancer Progression

Author

Jen-Liang Su, Nan-Yung Hsu, Shine-Gwo Shiah, Nai-Wen Hsu, Mien-Chie Hung, Yang-Hao Yu, Yi-Wen Chang, Michael Hsiao, Ching-Yao Yang, Ching-Feng Chiu, Jean Chiou, Pai-Sheng Chen, and Chih-Chen Hong

Abstract

Supplementary Figure 5. The mRNA expression of Dicer between TE-1/VEGF-C and TE-1/vector cells. TE-1 cells were stably expressed VEGF-C or vector control and isolated total RNA to assay. qRT-PCR was used to quantify the expression level of Dicer, and the quantities values were evaluated as percentage compared with TE-1/vector cells. The columns are the mean values from three independent experiments. Bars indicate means {plus minus} SE.

Published
2023
Full Text
View/download PDF

40. Supplementary Figure 2 from miR326 Maturation Is Crucial for VEGF-C–Driven Cortactin Expression and Esophageal Cancer Progression

Author

Jen-Liang Su, Nan-Yung Hsu, Shine-Gwo Shiah, Nai-Wen Hsu, Mien-Chie Hung, Yang-Hao Yu, Yi-Wen Chang, Michael Hsiao, Ching-Yao Yang, Ching-Feng Chiu, Jean Chiou, Pai-Sheng Chen, and Chih-Chen Hong

Abstract

Supplementary Figure 2. The phosphorylated VEGFR-3 in TE-1 cells with VEGF-C and VEGF-A treatment. TE-1 cells were starved with serum-free medium for 16 hours and treated with 100 and 200 ng/mL VEGF-C as well as 200 ng/mL VEGF-A for 10 minutes, following examination of phosphorylated VEGFR-3 (p-VEGFR-3, Y1063/1068) expression by Western blot. β-actin was used as an internal control.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results