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Joint 2D-3D Breast Cancer Classification [article]

Gongbo Liang, Xiaoqin Wang, Yu Zhang, Xin Xing, Hunter Blanton, Tawfiq Salem, Nathan Jacobs
2020 arXiv   pre-print
Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously.  ...  Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and  ...  [12] proposed an end-to-end breast cancer classification method using AlexNet [18] as the backbone.  ... 
arXiv:2002.12392v1 fatcat:vm7en2gurrforapvd7y6i2lhvu

Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

Xiangrong Zhou, Takuya Kano, Yunliang Cai, Shuo Li, Xinxin Zhou, Takeshi Hara, Ryujiro Yokoyama, Hiroshi Fujita, Georgia D. Tourassi, Samuel G. Armato
2016 Medical Imaging 2016: Computer-Aided Diagnosis  
Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.  ...  The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images.  ...  local and partial 2D information of a 3D image redundantly.  ... 
doi:10.1117/12.2217256 dblp:conf/micad/ZhouKCLZHY016 fatcat:wkqljetngzepnijcgpd2mas44e

Front Matter: Volume 9785

2016 Medical Imaging 2016: Computer-Aided Diagnosis  
1V Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method [9785-66] 9785 1W Quantitative breast MRI radiomics for cancer  ...  [9785-73] 9785 23 First and second-order features for detection of masses in digital breast tomosynthesis [9785-74] 9785 24 An adaptive online learning framework for practical breast cancer diagnosis  ... 
doi:10.1117/12.2240961 dblp:conf/micad/X16 fatcat:b5addnksdrgp3ixwvbjt53xeqe

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor

Prateek Prasanna, Pallavi Tiwari, Anant Madabhushi
2016 Scientific Reports  
on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas  ...  Another example is triple negative (TN) breast cancer (highly aggressive) and fibroadenomas (FA) (benign tumor) with similar morphologic appearances on MRI 2 .  ...  Breast cancer dataset.  ... 
doi:10.1038/srep37241 pmid:27872484 pmcid:PMC5118705 fatcat:73uvqgxbxjbgtninuwovf2jocu

Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images [article]

Fengying Che, Ruichuan Shi, Jian Wu, Haoran Li, Shuqin Li, Weixing Chen, Hao Zhang, Zhi Li, Xiaoyu Cui
2021 arXiv   pre-print
Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static features.  ...  Meanwhile, 2D and 3D features are often used in combination and have shown a better performance than that from 2D or 3D alone [22] .  ...  Feature 3D M ulti 0.528 0.556 0.611 2D M ulti 0.639 0.500 0.528 3D 0.681 0.639 0.651 2D 0.528 0.611 0.527 Fig. 4.  ... 
arXiv:2011.00454v3 fatcat:ncehshs2orgh7ftsdcnypq2r2q

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes 442 Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual  ...  PET+MRI Patch-based Dictionary for Bayesian Random Field PET Reconstruction 520 Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Front Matter: Volume 9786

2016 Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling  
9786 0A Automatic masking for robust 3D-2D image registration in image-guided spine surgery 9786 0B Robust patella motion tracking using intensity-based 2D-3D registration on dynamic bi-plane fluoroscopy  ...  planning 9786 06 Fusion of CTA and XA data using 3D centerline registration for plaque visualization during coronary intervention SESSION 2 SEGMENTATION AND 2D AND 3D REGISTRATION 07 Random walk  ... 
doi:10.1117/12.2240097 dblp:conf/miigp/X16 fatcat:e2sr443rvfa7rcz24tfpgb44gy

Computerized Assessment of Breast Lesion Malignancy using DCE-MRI

Weijie Chen, Maryellen L. Giger, Gillian M. Newstead, Ulrich Bick, Sanaz A. Jansen, Hui Li, Li Lan
2010 Academic Radiology  
We used a Bayesian neural network with automatic relevance determination for joint feature selection and classification.  ...  Conclusion-These results demonstrate the robustness of our computerized classification system in the task of distinguishing between malignant and benign breast lesions on DCE-MRI images from two manufacturers  ...  of the initial breast-cancer diagnosis (3) .  ... 
doi:10.1016/j.acra.2010.03.007 pmid:20540907 pmcid:PMC2907891 fatcat:2s3ohvyrejd45k6e2o56aoflzu

A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment

Hélder P. Oliveira, Jaime S. Cardoso, André T. Magalhães, Maria J. Cardoso
2013 Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization  
Breast cancer conservative treatment (BCCT) is now the preferred technique for breast cancer treatment.  ...  The aim is to enable the automatic joint detection of prominent points, both on depth and RGB images.  ...  Introduction Breast cancer is the most common cancer that affects women in the world.  ... 
doi:10.1080/21681163.2013.858403 fatcat:e76d64kfdjevhd77scby6wjq7y

Front Matter: Volume 9414

2015 Medical Imaging 2015: Computer-Aided Diagnosis  
breast tomosynthesis [9414-42] 9414 0M Signal enhancement ratio (SER) quantified from breast DCE-MRI and breast cancer risk [9414-21] 9414 0N A comparative analysis of 2D and 3D CAD for calcifications  ...  17 Local mammographic density as a predictor of breast cancer [9414-20] 9414 18 Digital breast tomosynthesis: application of 2D digital mammography CAD to detection of microcalcification clusters  ...  The applications ranged from detection, characterization, treatment monitoring, surgery, segmentation, and visualization aids in breast, lung, brain, abdomen, bladder, and spine imaging.  ... 
doi:10.1117/12.2194210 dblp:conf/micad/X15 fatcat:wzirgkwiwbgvba6jlaucm3azzq

3D Ultrasound image segmentation: A Survey [article]

Mohammad Hamed Mozaffari, WonSook Lee
2016 arXiv   pre-print
Some of 3D prostate segmentation methods summary is tabulated in Breast Cancer According to [28] the deadliest cancer among women is Breast Cancer.  ...  To extract 3D shape model of breast cancer from 3D US data, Chang et al. [31] used 3D Snake Models (Active Contour Models).  ... 
arXiv:1611.09811v1 fatcat:gwpegdj7ifhztidyksxy7e6h2q

Front Matter: Volume 11597

Karen Drukker, Maciej A. Mazurowski
2021 Medical Imaging 2021: Computer-Aided Diagnosis  
feasibility in interim analysis of the ECOG-ACRIN E4112 trial 11597 0C Electronic removal of lesions for more robust BPE scoring on breast DCE-MRI 11597 0D Comparison of 2D and 3D U-Net breast lesion  ...  cancer risk assessment: evaluation of FFDM radiomic similarity [11597-32] 11597 10 Multi-task learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis [11597-34]  ...  Severity assessment of COVID-19 using imaging descriptors: a deep-learning transfer learning approach from non-COVID-19 pneumonia 11597 1U COVID-19 opacity segmentation in chest CT via HydraNet: a joint  ... 
doi:10.1117/12.2595447 fatcat:u25cvo7adbgcxb363rsnsgnsju

Application of a Three-Dimensional Reconstruction System in Breast Cancer with Ipsilateral Supraclavicular Lymph Node Metastasis: A Case Series

Piao Zhao, Qiongyan Zou, Liqin Yuan, Lun Li, Qitong Chen, Dengjie Ouyang, Wenjun Yi
2018 Breast Care  
We innovatively applied a three-dimensional (3D) reconstruction system to assess the feasibility of SCLND preoperatively for 13 breast cancer patients with ISLM.  ...  The role of supraclavicular lymph node dissection (SCLND) in breast cancer patients with ipsilateral supraclavicular lymph node metastasis (ISLM) remains controversial.  ...  Postoperative treatment was based on the molecular classification of the breast cancer tissues: 1 (7.7%) patient was luminal A, 5 (23.1%) patients were luminal B, 5 (23.1%) were HER2-positive, and 2 (15.4%  ... 
doi:10.1159/000492601 pmid:31316317 pmcid:PMC6600050 fatcat:o5de5pr6gbbx5h3ysytkaoocfy

Front Matter: Volume 9790

2016 Medical Imaging 2016: Ultrasonic Imaging and Tomography  
segmentation for assistant diagnosis of breast cancer [9790-36] 9790 12 Development of estimation system of knee extension strength using image features in ultrasound images of rectus femoris [9790  ...  cells in scanning probe acoustic microscope: a preliminary study [9790-65] 9790 1U A preliminary evaluation work on a 3D ultrasound imaging system for 2D array transducer [9790-66] 9790 1V A new post-phase  ... 
doi:10.1117/12.2240428 fatcat:mf56ppgnvvblbcw3tmbox75s64

Artificial Intelligence (AI)-based Medical Image Segmentation for 3D Printing and Naked Eye 3D Visualization

Guang JIA, Xunan HUANG, Sen TAO, Xianghuai ZHANG, Yue ZHAO, Hongcai WANG, Jie HE, Jiaxue HAO, Bo LIU, Jiejing ZHOU, Tanping LI, Xiaoling ZHANG (+1 others)
2021 Intelligent Medicine  
Breast displacement during each radiation therapy was quantitatively evaluated by automatically identifying the position of the 3D printed plastic breast bra.  ...  Prostate cancer and bladder cancer were segmented based on U-net from MRI images.  ...  CT images of patients with breast cancer were converted to 3D objects.  ... 
doi:10.1016/j.imed.2021.04.001 fatcat:akbe3djy2zhuxbeck4d6qlmuiy
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