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Medical Imaging: Computer-Aided Diagnosis 2020: Houston, TX, USA
- Horst K. Hahn, Maciej A. Mazurowski:
Medical Imaging 2020: Computer-Aided Diagnosis, Houston, TX, USA, February 16-19, 2020. SPIE Proceedings 11314, SPIE 2020, ISBN 9781510633957
Mammography
- Stefano Pedemonte:
A hypersensitive breast cancer detector (Conference Presentation). - Yifan Peng
, Rui Hou, Yinhao Ren, Lars J. Grimm, Jeffrey R. Marks, Eun-Sil Shelley Hwang, Joseph Y. Lo:
Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index. - Alexej Gossmann
Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift. - Rui Hou, Lars J. Grimm, Maciej A. Mazurowski, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, Eun-Sil Shelley Hwang, Joseph Y. Lo:
A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography. - Sadanand Singh, Thomas Paul Matthews, Meet Shah, Brent Mombourquette, Trevor Tsue, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su:
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis.
Chest I
- Angshuman Paul
Fast few-shot transfer learning for disease identification from chest x-ray images using autoencoder ensemble. - Anindo Saha
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. - Jia Liang, Yuxing Tang, Youbao Tang, Jing Xiao, Ronald M. Summers:
Bone suppression on chest radiographs with adversarial learning. - Jordan D. Fuhrman, Rowena Yip, Artit C. Jirapatnakul
Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT. - Simon Rennotte, Pierre-Yves Brillet
Comparison of CNN architectures and training strategies for quantitative analysis of idiopathic interstitial pneumonia. - Jennie Crosby, Thomas Rhines, Feng Li, Heber MacMahon, Maryellen L. Giger:
Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets.
Neuro I
- Arko Barman
Combining symmetric and standard deep convolutional representations for detecting brain hemorrhage. - Mina Rezaei, Tomoki Uemura, Janne Näppi, Hiroyuki Yoshida, Christoph Lippert, Christoph Meinel:
Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis. - Mikhail Milchenko, Pamela LaMontagne, Daniel S. Marcus:
Automatic detection of contrast enhancement in T1-weighted brain MRI of human adults. - Takuya Fuchigami, Sadato Akahori, Takayuki Okatani, Yuanzhong Li:
A hyperacute stroke segmentation method using 3D U-Net integrated with physicians' knowledge for NCCT. - Linmin Pei
Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction.
Abdomen
- Chenglong Wang, Masahiro Oda
Organ segmentation from full-size CT images using memory-efficient FCN. - Sai Aditya Sriram, Angshuman Paul
Multilevel UNet for pancreas segmentation from non-contrast CT scans through domain adaptation. - Li Liu, Jiang Tian, Cheng Zhong, Zhongchao Shi, Feiyu Xu:
Robust hepatic vessels segmentation model based on noisy dataset. - Amogh Hiremath, Rakesh Shiradkar, Nathaniel Braman, Prateek Prasanna
A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer. - Marc Jason Pomeroy, Yi Wang, Anushka Banerjee, Almas F. Abbasi, Matthew A. Barish, Edward Sun, Juan Carlos Bucobo, Perry J. Pickhardt, Zhengrong Liang:
Integration of optical and virtual colonoscopy images for enhanced classification of colorectal polyps.
Musculoskeletal
- Daniel C. Elton
Accurately identifying vertebral levels in large datasets. - Jimin Tan, Bofei Zhang, Kyunghyun Cho, Gregory Chang, Cem M. Deniz:
Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation. - Yan Wu
Deciphering tissue relaxation parameters from a single MR image using deep learning. - Nianyi Li, Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, William A. Jiranek, Maciej A. Mazurowski:
Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms. - Jakub Ceranka, Frédéric Lecouvet, Johan de Mey
Computer-aided detection of focal bone metastases from whole-body multi-modal MRI.
Radiomics
- Tomoki Uemura, Chinatsu Watari, Janne J. Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida:
U-radiomics for predicting survival of patients with idiopathic pulmonary fibrosis. - Joseph J. Foy, Inna H. Gertsenshteyn
Dependence of radiomics features on CT image acquisition and reconstruction parameters using a cadaveric liver. - Amrish Selvam, Jacob Antunes, Kaustav Bera, Asya Ofshteyn, Justin T. Brady, Katherine Bingmer, Kenneth Friedman, Sharon L. Stein, Rajmohan Paspulati, Andrei S. Purysko, Matthew Kalady, Anant Madabhushi, Satish E. Viswanath
Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers. - Raymond Joseph Acciavatti, Eric A. Cohen, Omid Haji Maghsoudi, Aimilia Gastounioti, Lauren Pantalone, Meng-Kang Hsieh, Emily F. Conant, Christopher G. Scott, Stacey J. Winham, Karla Kerlikowske, Celine Vachon, Andrew D. A. Maidment, Despina Kontos:
Robust radiomic feature selection in digital mammography: understanding the effect of imaging acquisition physics using phantom and clinical data analysis. - Heather M. Whitney, Maryellen L. Giger:
Improvement of classification performance using harmonization across field strength of radiomic features extracted from DCE-MR images of the breast. - Sehwa Moon, Dahim Choi, Ji-Yeon Lee, Myoung-Hee Kim, Helen Hong, Bong-Seog Kim, Jang-Hwan Choi:
Machine learning-powered prediction of recurrence in patients with non-small cell lung cancer using quantitative clinical and radiomic biomarkers.
Breast MRI, Skin
- Zachary Papanastasopoulos, Ravi K. Samala
Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI. - Karen Drukker
Deep learning predicts breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy. - Qiyuan Hu
Using ResNet feature extraction in computer-aided diagnosis of breast cancer on 927 lesions imaged with multiparametric MRI. - Bas H. M. van der Velden, Max A. A. Ragusi, Markus H. A. Janse, Claudette E. Loo, Kenneth G. A. Gilhuijs
Interpretable deep learning regression for breast density estimation on MRI. - Gourav Modanwal
MRI image harmonization using cycle-consistent generative adversarial network. - Nils Gessert, Marcel Bengs, Alexander Schlaefer
Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models. - Hamidullah Binol, M. Khalid Khan Niazi, Alisha Plotner, Jennifer Sopkovich, Benjamin Kaffenberger
A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning-based rosacea lesion identification.
Breast
- Ravi K. Samala
Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks. - Yue Li, Zheng Xie, Zilong He, Xiangyuan Ma, Yanhui Guo, Weiguo Chen, Yao Lu:
Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis. - Juhun Lee, Robert M. Nishikawa
Simulating breast mammogram using conditional generative adversarial network: application towards finding mammographically-occult cancer. - Chanho Kim, Won Hwa Kim, Hye Jung Kim, Jaeil Kim:
Weakly-supervised US breast tumor characterization and localization with a box convolution network. - Emine Doganay, Yahong Luo, Long Gao, Puchen Li, Wendie A. Berg, Shandong Wu:
Performance comparison of different loss functions for digital breast tomosynthesis classification using 3D deep learning model.
Chest II, Lymph Nodes
- Heike Carolus, Andra-Iza Iuga, Tom Brosch, Rafael Wiemker, Frank Thiele, Anna J. Höink, David Maintz, Michael Püsken, Tobias Klinder:
Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks. - Hidir Cem Altun, Grzegorz Chlebus, Colin Jacobs
Feasibility of end-to-end trainable two-stage U-Net for detection of axillary lymph nodes in contrast-enhanced CT based on sparse annotations. - Xiaomeng Gu, Weiyang Xie, Qiming Fang, Jun Zhao, Qiang Li:
Lung vessel suppression and its effect on nodule detection in chest CT scans. - Yifan Wang, Chuan Zhou, Heang-Ping Chan
Hybrid deep-learning model for volume segmentation of lung nodules in CT images. - Rohan Abraham, Ian Janzen, Saeed Seyyedi, Sukhinder Khattra, John Mayo, Ren Yuan, Renelle Myers, Stephen Lam, Calum E. MacAulay:
Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans (Conference Presentation). - Yannan Lin
EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography.
Keynote and Methodology
- Jonathan Wiener:
Will AI make me a better doctor? (Conference Presentation). - Vatsal Agarwal, Youbao Tang, Jing Xiao, Ronald M. Summers:
Weakly-supervised lesion segmentation on CT scans using co-segmentation. - Mehdi Moradi, Ken C. L. Wong, Alexandros Karargyris, Tanveer F. Syeda-Mahmood:
Quality controlled segmentation to aid disease detection.
Head and neck, eye
- Marcel Bengs, Stephan Westermann, Nils Gessert, Dennis Eggert, Andreas O. H. Gerstner, Nina A. Müller, Christian Betz, Wiebke Laffers, Alexander Schlaefer
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection. - Hamidullah Binol, Aaron C. Moberly, M. Khalid Khan Niazi, Garth Essig, Jay Shah, Charles Elmaraghy, Theodoros Teknos, Nazhat Taj-Schaal, Lianbo Yu, Metin N. Gurcan
Decision fusion on image analysis and tympanometry to detect eardrum abnormalities. - Friso G. Heslinga
Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from the Maastricht study. - Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich
Segmentation of retinal low-cost optical coherence tomography images using deep learning.
Novel Applications
- Khrystyna Faryna, Fakrul Islam Tushar
Attention-guided classification of abnormalities in semi-structured computed tomography reports. - Feng Yang, Nicolas Quizon, Hang Yu, Kamolrat Silamut, Richard James Maude
Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears. - Maysam Shahedi
Segmentation of uterus and placenta in MR images using a fully convolutional neural network. - Jiaxing Tan, Shu Zhang, Weiguo Cao, Yongfeng Gao, Lihong Li, Yumei Huo, Zhengrong Liang:
A multi-stage fusion strategy for multi-scale GLCM-CNN model in differentiating malignant from benign polyps. - Daniel Hoklai Chapman-Sung, Lubomir M. Hadjiiski, Dhanuj Gandikota, Heang-Ping Chan
Convolutional neural network-based decision support system for bladder cancer staging in CT urography: decision threshold estimation and validation.
Neuro II
- Seyed Saman Saboksayr, John J. Foxe, Axel Wismüller:
Attention-deficit/hyperactivity disorder prediction using graph convolutional networks. - Mariana Pereira, Irene Fantini, Roberto de Alencar Lotufo, Letícia Rittner
An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI. - Yashas Hiremath, Marwa Ismail, Ruchika Verma
Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus controls. - Tsubasa Goto, Caihua Wang, Yuanzhong Li, Yukihiro Tsuboshita:
Multi-modal deep learning for predicting progression of Alzheimer's disease using bi-linear shake fusion. - Axel Wismüller, John J. Foxe, Paul Geha, Seyed Saman Saboksayr:
Large-scale Extended Granger Causality (lsXGC) for classification of Autism Spectrum Disorder from resting-state functional MRI. - Rita Lai, Daniela Schenone
Prognostic power of the human psoas muscles FDG metabolism in amyotrophic lateral sclerosis.
Poster Session
- Basel Alyafi
DCGANs for realistic breast mass augmentation in x-ray mammography. - Michael Vieceli, Amy Van Dusen, Karen Drukker
Case-based repeatability of machine learning classification performance on breast MRI. - Peter S. Carras, Carina Pereira, Debosmita Biswas, Christoph I. Lee, Savannah C. Partridge, Adam M. Alessio:
Genetic algorithm for machine learning architecture selection for breast MRI classification. - Stijn De Buck
Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation. - Erick Rodríguez-Esparza
Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach. - Yuanpin Zhou
Generating high resolution digital mammogram from digitized film mammogram with conditional generative adversarial network. - Albert Swiecicki, Mateusz Buda, Ashirbani Saha, Nianyi Li, Sujata V. Ghate, Ruth Walsh
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images. - William Funke, Benjamin Veasey, Jacek M. Zurada, Hichem Frigui, Amir A. Amini:
3D U-Net for segmentation of pulmonary nodules in volumetric CT scans from multi-annotator truth estimation. - Yuki Suzuki, Kazuki Yamagata, Yanagawa Masahiro
Weak supervision in convolutional neural network for semantic segmentation of diffuse lung diseases using partially annotated dataset. - Colin B. Hansen, Yiyuan Zhao, Halid Ziya Yerebakan, Luca Bogoni, Anna K. Jerebko:
False positive reduction of vasculature for pulmonary nodule detection. - Hengtao Guo
Multi-task learning for mortality prediction in LDCT images. - Hidenobu Suzuki
Association analysis of SNPs with CT image-based phenotype of emphysema progression in heavy smokers. - Phuong Nguyen, David Chapman, Sumeet Menon, Michael Morris
Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data. - Panagiotis Gonidakis
Artificially augmenting data or adding more samples? A study on a 3D CNN for lung nodule classification. - Babak Haghighi, Peter B. Noël, Eric A. Cohen, Lauren Pantalone, Anil Vachani, Katharine A. Rendle
Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study. - Ryan Sullivan, Gregory Holste
Deep learning methods for segmentation of lines in pediatric chest radiographs. - Takeru Kageyama, Yoshiki Kawata, Noboru Niki, Masahiko Kusumoto, Yoshiki Aokage, Genichiro Ishii, Hironobu Ohmatsu, Takaaki Tsuchida, Yuji Matsumoto, Kenji Eguchi, Masahiro Kaneko:
Differential diagnosis of pulmonary nodules using 3D CT images. - Jorie D. Budzikowski, Ahmed A. Rashid, Joseph J. Foy, Jonathan H. Chung, Imre Noth, Samuel G. Armato III:
Radiomics-based texture analysis of idiopathic pulmonary fibrosis for genetic and survival predictions. - Sohyun Byun, Julip Jung, Helen Hong, Hoonil Oh, Bong-seog Kim:
Lung tumor segmentation using coupling-net with shape-focused prior on chest CT images of non-small cell lung cancer patients. - Dong Wei, Yiming Li, Yinyan Wang, Tianyi Qian, Yefeng Zheng
Deep convolutional neural networks for molecular subtyping of gliomas using magnetic resonance imaging. - Mohammad Mahdi Shiraz Bhurwani
Predicting treatment outcome of intracranial aneurysms using angiographic parametric imaging and recurrent neural networks. - Jorge Orozco-Sanchez
Prediction of MCI to AD risk of conversion survival models: qMRI vs CSF measures and cognitive assessments. - Haolun Li, Rui Zong, Xin Xu, Longsheng Pan, Qionghai Dai, Feng Xu, Hao Gao, Wensheng Wang:
Diagnosis of Parkinson's Disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. - Evan D. H. Gates, Jonathan S. Lin, Jeffrey S. Weinberg, Sujit S. Prabhu, Jackson Hamilton
Advanced magnetic resonance imaging based algorithm for local grading of glioma. - Ronald M. Juarez-Chambi, Carmen Kut, Kaisorn L. Chaichana, Alfredo Quinones-Hinojosa, Xingde Li, Javier A. Jo:
Neural networks for in situ detection of glioma infiltration using optical coherence tomography. - Hooman Rokham
A data-driven approach for stratifying psychotic and mood disorders subjects using structural magnitude resonance imaging data. - Tatsat R. Patel
Multi-resolution CNN for brain vessel segmentation from cerebrovascular images of intracranial aneurysm: a comparison of U-Net and DeepMedic. - Yang Lei
Automatic detection of brain metastases using 3D mask R-CNN for stereotactic radiosurgery. - Yabo Fu, Yang Lei
Automatic brain arteriovenous malformations segmentation on contrast CT images using combined region proposal network and V-Net. - Min Jin Lee, Helen Hong, Kyu Won Shim
Computer-assisted quantification of surgical outcome in infants with sagittal craniosynostosis in 3D head CT images using mean normal skull model. - Ramon Correa, Qiu Lei, Jonathan Chen
"Lesion-habitat" radiomics to distinguish radiation necrosis from tumor recurrence on post-treatment MRI in metastatic brain tumors. - Joost van der Putten, Jeroen de Groof, Fons van der Sommen
First steps into endoscopic video analysis for Barrett's cancer detection: challenges and opportunities. - Pritesh Mehta, Michela Antonelli, Hashim Uddin Ahmed, Mark Emberton, Shonit Punwani, Sébastien Ourselin
Decision fusion of 3D convolutional neural networks to triage patients with suspected prostate cancer using volumetric biparametric MRI. - Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Hizuru Amano, Aitaro Takimoto, Hiroo Uchida
Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images. - Tomoki Uemura, Janne J. Näppi, Toru Hironaka, Hyoungseop Kim, Hiroyuki Yoshida:
Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography. - Binu Enchakolody, Brianna Henderson, Stewart C. Wang
Machine learning methods to predict presence of intestine damage in patients with Crohn's disease. - Levi Verhage, Joost van der Putten, Fons van der Sommen
The field effect in Barrett's Esophagus: a macroscopic view using white light endoscopy and deep learning. - Hayato Itoh, Zhongyang Lu, Yuichi Mori, Masashi Misawa, Masahiro Oda
Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis. - Lili Wang, Jie Wu, Guang Yang, Bin Zheng:
Computer-aided staging of gastric cancer using radiomics signature on computed tomography imaging. - Diego Bravo
Automatic polyp detection and localization during colonoscopy using convolutional neural networks. - David Liang, David Wang, Alice Wei, Yeseul Choi, Shu Zhang, Marc Jason Pomeroy, Perry J. Pickhardt:
Performance investigation of deep learning vs. classifier for polyp differentiation via texture features. - Janne J. Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, Hiroyuki Yoshida:
Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography. - Shu Zhang, Weiguo Cao, Marc Jason Pomeroy, Yongfeng Gao, Jiaxing Tan, Zhengrong Liang:
A deep learning based integration of multiple texture patterns from intensity, gradient and curvature GLCMs in differentiating the malignant from benign polyps. - Weiguo Cao, Marc Jason Pomeroy, Shu Zhang, Perry J. Pickhardt, Hongbing Lu, Zhengrong Liang:
Deformation robust texture features for polyp classification via CT colonography. - Ryan Alfano, Glenn S. Bauman, Jonathan D. Thiessen, Irina Rachinsky, William Pavlosky, John Butler, Mena Gaed, Madeleine Moussa, José A. Gómez, Joseph L. Chin, Stephen E. Pautler, Aaron D. Ward
Evaluating texture-based prostate cancer classification on multi-parametric magnetic resonance imaging and prostate specific membrane antigen positron emission tomography. - Rakesh Shiradkar, Ruyuan Zuo, Amr Mahran
Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results. - Julip Jung, Helen Hong, Tae-Sik Jeong, Jinsil Seong, Jin Sung Kim
Automatic liver segmentation in abdominal CT images using combined 2.5D and 3D segmentation networks with high-score shape prior for radiotherapy treatment planning. - Julip Jung, Helen Hong, Young-Gi Kim, Sung Il Hwang
Prediction of prostate cancer aggressiveness using quantitative radiomic features using multi-parametric MRI. - Hyeonjin Kim, Helen Hong, Koon Ho Rha
Renal parenchyma segmentation in abdominal CT images based on deep convolutional neural networks with similar atlas selection and transformation. - Michael I. Ivanitskiy, Lubomir M. Hadjiiski, Heang-Ping Chan
Bladder wall segmentation using U-net based deep learning. - Hansang Lee, Helen Hong, Jinsil Seong, Jin Sung Kim
Survival prediction of liver cancer patients from CT images using deep learning and radiomic feature-based regression. - Zbigniew Starosolski, J. Herman Kan, Ananth V. Annapragada:
CNN-based detection of distal tibial fractures in radiographic images in the setting of open growth plates. - Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, Mateusz Buda, Nianyi Li, William A. Jiranek, Maciej A. Mazurowski:
Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms. - Chen Li, Parmeet S. Bhatia, Yu Zhao:
Knee orientation detection in MR scout scans using 3D U-net. - Xiuxiu He, Bangjun Guo, Tonghe Wang
Classification of lesion specific myocardial ischemia using cardiac computed tomography radiomics. - Abhishaike Mahajan, James D. Dormer
Siamese neural networks for the classification of high-dimensional radiomic features. - Peter F. Michael, Hong-Jun Yoon:
Survey of image denoising methods for medical image classification. - Alexander Sóñora-Mengan
Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework. - Aliasghar Mortazi, Jayaram K. Udupa, Yubing Tong, Drew A. Torigian:
A post-acquisition standardization method for positron emission tomography images. - Yuichiro Hayashi, Chen Shen, Holger R. Roth, Masahiro Oda
Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset. - Yan Li, Jun Wei, Zhenyu Qi, Ying Sun, Yao Lu:
Synthesize CT from paired MRI of the same patient with patch-based generative adversarial network. - Shuang Gao, Vince D. Calhoun
Multi-modal component subspace-similarity-based multi-kernel SVM for schizophrenia classification. - Meysam Tavakoli, Mahdieh Nazar, Alireza Mehdizadeh
The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images. - Hong Kang, Xiaoxing Li, Xiu Su:
Cup-disc and retinal nerve fiber layer features fusion for diagnosis glaucoma. - Bangjun Guo, Xiuxiu He, Tonghe Wang
Benign and malignant thyroid classification using computed tomography radiomics. - Yujiao Xia, Xinyao Cheng, Aaron Fenster, Mingyue Ding:
Automatic classification of carotid ultrasound images based on convolutional neural network. - Sarah Bi, Laura Martinez, Justin Bequette, Andrew Peitzsch
Verification of accuracy of an algorithmic image-based dental pulp vitality test. - Yang Lei
Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN. - Xiuxiu He, Bangjun Guo, Yang Lei
3D thyroid segmentation in CT using self-attention convolutional neural network. - Masahiro Oda
Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique. - Joseph Cox
Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images. - Yiyang Wang
Detecting age-related macular degeneration (AMD) biomarker images using MFCC and texture features. - Daniela Schenone
Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma. - Yue Sun, Deedee Kommers, Tao Tan, Wenjin Wang, Xi Long
Automated discomfort detection for premature infants in NICU using time-frequency feature-images and CNNs. - Yi Yin
Standardization of blood flow measurements by automated vascular analysis from power Doppler ultrasound scan. - Alexander R. Podgorsak, Kelsey N. Sommer, Vijay Iyer, Michael F. Wilson, Frank J. Rybicki, Dimitrios Mitsouras, Umesh Sharma, Kanako K. Kumamaru, Erin Angel, Ciprian N. Ionita:
Investigation of the accuracy of classifying coronary artery disease severity using machine learning with subdomain analysis of fractional flow reserve diagnosis in patients. - Peilun Song, Yaping Wang, Xiujuan Geng
Investigation of sex hormones on the early diagnosis of schizophrenia. - Yabo Fu, Bangjun Guo, Yang Lei
Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography. - Ghada Zamzmi, Li-Yueh Hsu
Fully automated spectral envelope and peak velocity detection from Doppler echocardiography images.
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