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| 1 | 基于视觉感知的图像检索的研究显示文摘基于内容图像检索的一个突出问题是图像低层特征与高层语义之间存在的巨大鸿沟.针对相关反馈和感兴趣区检测在弥补语义鸿沟时存在主观性强、耗时的缺点,提出了视觉信息是一种客观反映图像高层语义的新特征,基于视觉信息进行图像检索可以有效减小语义鸿沟;并在总结视觉感知的研究进展和实现方法的基础上,给出了基于视觉感知的图像检索在感兴趣区检测、图像分割、相关反馈和个性化检索四个方面的研究思路. | 张菁 沈兰荪 David Dagan Feng | 2008 | 电子学报2008，36，3： | 32 |
| 2 | The combined therapeutic effects of ^(131)iodinelabeled multifunctional copper sulfide-loaded microspheres in treating breast cancer显示文摘Compared to conventional cancer treatment, combination therapy based on well-designed nanoscale platforms may offer an opportunity to eliminate tumors and reduce recurrence and metastasis.In this study, we prepared multifunctional microspheres loading ^(131)I-labeled hollow copper sulfide nanoparticles and paclitaxel( ^(131)I-HCu SNPs-MS-PTX) for imaging and therapeutics of W256/B breast tumors in rats.18 F-fluordeoxyglucose(18 F-FDG) positron emission tomography/computed tomography(PET/CT) imaging detected that the expansion of the tumor volume was delayed(Po0.05) following intra-tumoral(i.t.) injection with ^(131)I-HCu SNPs-MS-PTX plus near-infrared(NIR) irradiation. The immunohistochemical analysis further confirmed the anti-tumor effect. The single photon emission computed tomography(SPECT)/photoacoustic imaging mediated by ^(131)I-HCu SNPs-MS-PTX demonstrated that microspheres were mainly distributed in the tumors with a relatively low distribution in other organs. Our results revealed that ^(131)I-HCu SNPs-MS-PTX offered combined photothermal, chemo-and radio-therapies, eliminating tumors at a relatively low dose, as well as allowing SPECT/CT and photoacoustic imaging monitoring of distribution of the injected agents non-invasively. The copper sulfide-loaded microspheres, ^(131)I-HCu SNPs-MS-PTX, can serve as a versatile theranostic agent in an orthotopic breast cancer model. | Qiufang Liu Yuyi Qian Panli Li Sihang Zhang Zerong Wang Jianjun Liu Xiaoguang Sun Michael Fulham Dagan Feng Zhigang Chen Shaoli Song Wei Lu Gang Huang | 2018 | Acta Pharmaceutica Sinica B2018，8，3： | 3 |
| 3 | A Review of Predictive and Contrastive Self-supervised Learning for Medical Images显示文摘Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain. | Wei-Chien Wang Euijoon Ahn Dagan Feng Jinman Kim | 2023 | Machine Intelligence Research2023，20，4： | 2 |
| 4 | Regularized image reconstruction with an anatomically adaptive prior for positron emission tomography显示文摘 | Chung Chan Roger Fulton David Dagan Feng Steven Meikle | 2009 | Physics in Medicine and Biology2009，54，： | 1 |
| 5 | Adptive segmentation of textured images by using the coupled Markov random field model显示文摘 | Yong Xia Dagan Feng Rongchun Zhao | 2006 | IEEE Transactions on Image Processing2006，15，11： | 1 |
| 6 | Toxicological approach for assessing the heavy metal binding capacity of soils 显示文摘 | Feng N Dagan R Bitton G | 2007 | Soil&sediment contamination2007，16，5： | 1 |
| 7 | A PERSONALIZED IMAGE RETRIEVAL BASED ON VISUAL PERCEPTION显示文摘A new scheme named personalized image retrieval technique based on visual perception is proposed in this letter, whose motive is to narrow the semantic gap by directly perceiving user's visual information. It uses visual attention model to segment image regions and eye-tracking technique to record fixations. Visual perception is obtained by analyzing the fixations in regions to measure gaze interests. Integrating visual perception into attention model is to detect the Regions Of Interest (ROIs), whose features are extracted and analyzed, then feedback interests to optimize the results and construct user profiles. | Zhang Jing Shen Lansun David Dagan Feng | 2008 | Journal of Electronics(China)2008，25，1： | 1 |
| 8 | Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model显示文摘 | Yong Xia Dagan Feng Rongchun Zhao | 2006 | IEEE Transactions On Image Processi ng2006，15，11： | 1 |
| 9 | Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography 显示文摘 | FENG Dagan HUANG Sungcheng WANG Xinmin | 1993 | Int J Biomed Comput1993，32，2： | 1 |
| 10 | Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model 显示文摘 | Xia Yong Feng Dagan Zhao Rongchun | 2006 | IEEE Transactions on Image Processing2006，15，11： | 1 |
| 11 | Robust and efficient contentbased digital audio watermarking显示文摘 | Changsheng Xu David Dagan Feng | 2002 | Multimedia Systems2002，8，5： | 1 |
| 12 | Generalized rough fuzzy c-means algorithm for brain MR image segmentation显示文摘 | Zexuan Ji Quansen Sun Yong Xia Qiang Chen Deshen Xia Dagan Feng | 2011 | Computer Methods and Programs in Biomedicine2011，，2： | 1 |
| 13 | Region-based reconstruction method for fluorescent molecular tomography 显示文摘 | Zou Wei Wang Jiajun Feng David Dagan | 2010 | J Opt Soc Am A2010，27，10： | 1 |
| 14 | Image segmentation by clustering of spatial patterns显示文摘 | Yong Xia (David) Dagan Feng Tianjiao Wang Rongchun Zhao Yanning Zhang | 2007 | Pattern Recognition Letters2007，，12： | 1 |
| 15 | Attention-driven image interpretation with application to image retrieval显示文摘 | Fu Hong Chi Zheru Feng Dagan | 2006 | Pattern Recognition2006，39，9： | 1 |
| 16 | Image Segmentation by Clustering of Spatial Patterns显示文摘 | Xia Yong Feng Dagan Wang Tianjiao | 2007 | Pattern Recognition Letters2007，28，12： | 1 |
| 17 | Texture image classification with discriminative neural networks显示文摘Texture provides an important cue for many computer vision applications, and texture image classification has been an active research area over the past years. Recently, deep learning techniques using convolutional neural networks(CNN) have emerged as the state-of-the-art: CNN-based features provide a significant performance improvement over previous handcrafted features. In this study, we demonstrate that we can further improve the discriminative power of CNN-based features and achieve more accurate classification of texture images. In particular, we have designed a discriminative neural network-based feature transformation(NFT) method, with which the CNN-based features are transformed to lower dimensionality descriptors based on an ensemble of neural networks optimized for the classification objective. For evaluation, we used three standard benchmark datasets(KTH-TIPS2, FMD, and DTD)for texture image classification. Our experimental results show enhanced classification performance over the state-of-the-art. | Yang Song Qing Li Dagan Feng Ju Jia Zou Weidong Cai | 2016 | Computational Visual Media2016，2，4： | 1 |
| 18 | Estimation of myocardial glucose utilization with PET using the left ventricular time-activity curve as a non-invasive input function 显示文摘 | LI Xianjin FENG Dagan LIN Kangping | 1998 | Medical & Biological Engineering & Computing1998，36，1： | 1 |
| 19 | Recognition of attentive objects with a concept association network for image annotation显示文摘 | Hong Fu Zheru Chi Dagan Feng | | 0，，10： | 1 |
| 20 | Mixture Analysis by Multichannel Hopfield Neural Network显示文摘 | Mei Shaohui He Mingyi Wang Zhiyong Dagan Feng | 2010 | IEEE Geoscience and Remote Sensing Letters2010，7，3： | 1 |
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