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Computer-Aided Diagnosis 2021
- Maciej A. Mazurowski, Karen Drukker:
Medical Imaging 2021: Computer-Aided Diagnosis, Online, February 15-20, 2021. SPIE Proceedings 11597, SPIE 2021, ISBN 9781510640238
Keynote
- Saurabh Jha:
Decoding Radiology: A Brief History.
Lung I
- Qiyuan Hu
, Karen Drukker
Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning. - Rahul Paul, Sherzod Kariev, Dmitry Cherezov, Matthew B. Schabath, Robert J. Gillies
Deep radiomics: deep learning on radiomics texture images. - Dalton Griner, Ran Zhang, Xin Tie
COVID-19 pneumonia diagnosis using chest x-ray radiograph and deep learning. - Yiting Xie, Deepta Rajan, Larissa C. Schudlo, Yusuke Takeuchi, Benedikt Graf, Adam Coy, Mohammadreza Negahdar, Vandana V. Mukherjee, David Beymer, Arun Krishnan:
Automatic localization of lung opacity in chest CT images: a real-world study. - Catalin I. Fetita, Simon Rennotte, Marjorie Latrasse, Ruxandra Tapu, Mathilde Maury, Bogdan Mocanu, Hilario Nunes, Pierre-Yves Brillet
Transferring CT image biomarkers from fibrosing idiopathic interstitial pneumonia to COVID-19 analysis. - Connor Cowan, Joseph Bae, Gagandeep Singh, Rohit Khullar, Shrey Shah, Nikhil Madan, Prateek Prasanna:
Evolution of chest radiograph radiomics and association with respiratory and inflammatory parameters in COVID-19 patients undergoing prone ventilation: preliminary findings. - Rafael B. Fricks, Ehsan Abadi, Francesco Ria
Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images.
Breast I
- Vivian Belenky, Rhea Chitalia, Nickolas Lewis, Debosmita Biswas, Constantine Gatsonis, Jennifer Xiao, Michael Hirano, Sunil Badve, Joseph A. Sparano, Seema Khan, Kathy D. Miller, Constance Lehman, Justin Romanoff, Antonio C. Wolff, Christopher Comstock, Savannah C. Partridge, Habib Rahbar, Despina Kontos:
Intrinsic radiomics phenotypes of DCI from breast DCE-MRI: demonstrating feasibility in interim analysis of the ECOG-ACRIN E4112 trial. - Lindsay Douglas
Electronic removal of lesions for more robust BPE scoring on breast DCE-MRI. - Roma Bhattacharjee, Lindsay Douglas
Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI. - Sokratis Makrogiannis
Sparse analysis of deep features for characterization of breast masses. - Stefania L. Moroianu, Mirabela Rusu
Detecting invasive breast carcinoma on dynamic contrast-enhanced MRI.
Abdomen I
- Yabo Fu, Yang Lei
Deformable MRI-CT liver image registration using convolutional neural network with modality independent neighborhood descriptors. - Shadab Momin, Yang Lei
Deep learning-based deformable MRI-CBCT registration of male pelvic region. - Janne J. Näppi, Tomoki Uemura, Perry J. Pickhardt, David H. Kim, Hiroyuki Yoshida:
3D deep learning for computer-aided detection of serrated polyps in CT colonography. - Thomas DeSilvio, Stefania Moroianu, Indrani Bhattacharya
Intensity normalization of prostate MRIs using conditional generative adversarial networks for cancer detection. - Saumya Gupta, Venkata Suryanarayana K., Srinivas Rao Kudavelly, G. A. Ramaraju:
Ovarian assessment using deep learning-based 3D ultrasound super resolution. - Wankang Zeng, Wenkang Fan, Zhuohui Zheng, Rong Chen, Song Zheng, Jianhui Chen, Rong Liu, Zengqin Liu, Yinran Chen, Xiongbiao Luo:
Abdominal CT urography kidney segmentation using spatiotemporal fully convolutional network.
Cardiovascular and Ophthalmology
- Jiayang Zhong, Zhangxing Bian
Segmentation of the thoracic aorta using an attention-gated U-Net. - Ida Arvidsson, Niels Christian Overgaard, Anette Davidsson, Jeronimo Frias Rose, Kalle Åström
Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks. - Ziga Bizjak, Bostjan Likar, Franjo Pernus, Ziga Spiclin:
Modality agnostic intracranial aneurysm detection through supervised vascular surface classification. - Yinzhe Wu, Suzan Hatipoglu, Diego Alonso-Álvarez
Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR. - Tharindu S. De Silva, Gopal Jayakar, Peyton Grisso, Emily Y. Chew, Nathan Hotaling, Catherine A. Cukras:
Automatic detection of ellipsoid zone loss due to Hydroxychloroquine retinal toxicity from SD-OCT imaging.
Lung II
- Xiaomeng Gu, Fucai Chen, Weiyang Xie, Jun Zhao, Qiang Li:
Lung nodule malignancy prediction in chest CT scans based on a CNN model with auxiliary task learning. - Marjorie Latrasse, Simon Rennotte, Pierre-Yves Brillet
Traction bronchiectasis identification and quantitative biomarkers for follow-up in nonspecific interstitial pneumonia. - Yupei Zhang, Yang Lei
Region of interest discovery using discriminative concrete autoencoder for COVID-19 lung CT images. - Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Gopichandh Danala
Detecting COVID-19 infected pneumonia from x-ray images using a deep learning model with image preprocessing algorithm.
Breast II
- Yen Nhi Truong Vu, Trevor Tsue, Jason Su, Sadanand Singh:
An improved mammography malignancy model with self-supervised learning. - Paul Amstutz, Karen Drukker
Case-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions. - Juhun Lee, Robert M. Nishikawa
Estimating near term breast cancer risk from sequential mammograms using deep learning, radon cumulative distribution transform, and a clinical risk factor: preliminary analysis. - Natalie M. Baughan, Hui Li, Li Lan, Chun-Wai Chan, Matthew Embury, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen L. Giger:
Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity. - Giacomo Nebbia, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu:
Multi-task learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis. - Najmeh Mashhadi, Abolfazl Zargari Khuzani, Morteza Heidari, Donya Khaledyan
Applying a new feature fusion method to classify breast lesions.
Musculoskeletal
- Jun Luo, Gene Kitamura, Emine Doganay, Dooman Arefan, Shandong Wu:
Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images. - Qian Cao, Nicholas Petrick, Stephanie Coquia, Kenny H. Cha, Rongping Zeng, Keith Wear, Berkman Sahiner, Qin Li:
Assessment of bone fragility in projection images using radiomic features. - Uju Jeon, Hyeonjin Kim, Helen Hong, Joon-Ho Wang:
Two-stage meniscus segmentation framework integrating multiclass localization network and adversarial learning-based segmentation network in knee MR images. - Ien Li
Multi-resolution deep network ensembles for cervical intervertebral disc segmentation are biased by trainer. - Chuan Zhou, Heang-Ping Chan
Deep learning-based risk stratification for treatment management of multiple myeloma with sequential MRI scans.
Pediatric/fetal Applications
- Lan Min, Yue Sun, Peter H. N. de With:
Video-based infant monitoring using a CNN-LSTM scheme. - Junior Arroyo, Ana Cecilia Saavedra
Automatic fetal presentation diagnosis from ultrasound images for rural zones: head location as an indicator for fetal presentation.
Methodology
- David Sriker, Dana Cohen, Noa Cahan, Hayit Greenspan:
Improved segmentation by adversarial U-Net. - Kai Jiang, Hayato Itoh, Masahiro Oda
Dense-layer-based YOLO-v3 for detection and localization of colon perforations. - Xianjin Dai, Yang Lei
Deep attention mask regional convolutional neural network for head-and-neck MRI multi-organ auto-delineation. - Ken C. L. Wong, Elena S. Sinkovskaya, Alfred Z. Abuhamad, Tanveer F. Syeda-Mahmood:
Multiview and multiclass image segmentation using deep learning in fetal echocardiography. - Hui Meng, Qingfeng Li
Multi-scale view-based convolutional neural network for breast cancer classification in ultrasound images.
Neuroradiology including Head and neck
- Jie Mei
Differentiation of Parkinson's disease and non-Parkinsonian olfactory dysfunction with structural MRI data. - Mohammad Mahdi Shiraz Bhurwani, Kenneth V. Snyder, Mohammad Waqas, Maxim Mokin, Ryan A. Rava, Alexander R. Podgorsak, Kelsey N. Sommer, Jason M. Davies, Elad I. Levy, Adnan H. Siddiqui, Ciprian N. Ionita:
Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy. - Axel Wismüller, M. Ali Vosoughi:
Classification of schizophrenia from functional MRI using large-scale extended Granger causality. - Weiyao Wang, Aniruddha Tamhane, John R. Rzasa, James H. Clark, Therese L. Canares, Mathias Unberath:
Otoscopy video screening with deep anomaly detection. - Mingquan Lin, Shadab Momin, Boran Zhou, Katherine Tang, Yang Lei
Fully automated segmentation of brain tumor from multiparametric MRI using 3D context U-Net with deep supervision. - Reza Seifabadi, Fereshteh Aalamifar, S. Hossein Hezaveh, Can Kocabalkanli, Marius George Liguraru:
Quantitative assessment of deformational plagiocephaly and brachycephaly at the point-of-care.
Abdomen II
- Lubomir M. Hadjiiski, Monika Joshi, Ajjai Alva, Heang-Ping Chan
Multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography with and without computerized decision support. - Tonghe Wang
MRI-based prostate and dominant lesion segmentation using deep neural network. - Amir Bolous, Arun Seetharaman, Indrani Bhattacharya
Clinically significant prostate cancer detection on MRI with self-supervised learning using image context restoration. - Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Yudai Tamada
Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization. - Pravda Jith Ray Prasad, Ole Jakob Elle, Frank Lindseth, Fritz Albregtsen, Rahul Prasanna Kumar:
Modifying U-Net for small dataset: a simplified U-Net version for liver parenchyma segmentation.
Lung III
- Noa Cahan, Edith M. Marom, Shelly Soffer, Yiftach Barash, Eli Konen, Eyal Klang, Hayit Greenspan:
RV strain classification from 3D CTPA scans using weakly supervised residual attention model. - Yifan Wang, Chuan Zhou, Lei Ying
Reinforced learning from serial CT to improve the early diagnosis of lung cancer in screening. - Ran Zhang, Guang-Hong Chen:
Overcoming the "catastrophic forgetting" effect in transfer learning to achieve vendor independent performance for the COVID-19 pneumonia classification task using chest x-ray radiographs. - Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap:
Self-training with improved regularization for sample-efficient chest x-ray classification. - Ravi K. Samala
Severity assessment of COVID-19 using imaging descriptors: a deep-learning transfer learning approach from non-COVID-19 pneumonia. - Nimrod Sagie, Shiri Almog, Ayelet Talby, Hayit Greenspan:
COVID-19 opacity segmentation in chest CT via HydraNet: a joint learning multi-decoder network.
Poster Session
- Xiangrong Zhou, Seiya Yamagishi, Takeshi Hara, Hiroshi Fujita:
A hybrid approach for mammary gland segmentation on CT images by embedding visual explanations from a deep learning classifier into a Bayesian inference. - Thomas E. Tavolara, Adam M. Jorgensen, Metin N. Gurcan
Panoptic segmentation of wounds in a pig model. - Takumi Morishita, Chisako Muramatsu, Xiangrong Zhou, Ryo Takahashi, Tatsuro Hayashi, Wataru Nishiyama, Takeshi Hara, Yoshiko Ariji, Eiichiro Ariji
Tooth recognition and classification using multi-task learning and post-processing in dental panoramic radiographs. - Wenxi Yu, Hua Zhou
An automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using domain knowledge-guided attention models in HRCT images. - Zhongyang Lu, Masahiro Oda
Extremely imbalanced subarachnoid hemorrhage detection based on DenseNet-LSTM network with class-balanced loss and transfer learning. - José Marcio Luna, Andrew R. Barsky, Russell T. Shinohara, Alexandra D. Dreyfuss, Leonid Roshkovan
Radiomic features predict local failure-free survival in stage III NSCLC adenocarcinoma treated with chemoradiation. - Hoang Long Le, Yejin Jeon, Hong Gee Roh, Hyun Jeong Kim, Jin Tae Kwak
3-D multitask deep neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion. - Chirag Agarwal, Shahin Khobahi, Dan Schonfeld, Mojtaba Soltanalian
CoroNet: a deep network architecture for enhanced identification of COVID-19 from chest x-ray images. - Gal Gozes, Shani Ben Baruch, Noa Rotman-Nativ, Darina Roitshtain, Natan T. Shaked, Hayit Greenspan:
Deep learning analysis on raw image data: case study on holographic cell analysis. - Mohammad Ali Yektaie, Zahra Ghasemi, S. Hossein Hezaveh, Fereshteh Aalamifar, Reza Seifabadi, Marius George Linguraru:
Photographic cranial shape analysis using deep learning. - Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala
A novel feature reduction method to improve performance of machine learning model. - Noor Nakhaei, Chrysostomos Marasinou, Akinyinka Omigbodun, Nina Capiro, Bo Li, Anne Hoyt, William Hsu
Spatial matching of magnified 2D mammography images and specimen radiographs: towards improved characterization of suspicious microcalcifications. - Ka'Toria Edwards, Martin T. Halicek, James V. Little, Amy Y. Chen, Baowei Fei
Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images. - Zhe Zhu, Mustafa R. Bashir, Maciej A. Mazurowski:
Deep neural networks trained for segmentation are sensitive to brightness changes: preliminary results. - Yuichiro Hayashi, Masahiro Oda
Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks. - José Raniery Ferreira Jr.
A general fully automated deep-learning method to detect cardiomegaly in chest x-rays. - Naomi Joseph, Beth A. Benetz, Harry J. Menegay, Silke Oellerich
Early detection of at-risk keratoplasties and prediction of future corneal graft rejection from pre-diagnosis endothelial cell images. - Sven Kuckertz, Florian Weiler, Britta Matusche
A system for fully automated monitoring of lesion evolution over time in multiple sclerosis. - Zixiong Gao, Yufan Chen, Wuping Mai, Yao Lu, Shuyu Wu, Hongmei Liu:
Multi-task learning of perceptive feature for thyroid malignant probability prediction. - Gal Ofir, Dana Cohen:
Improving retinal images segmentation using styleGAN image augmentation. - Ahmed S. Maklad
Fully automatic bone segmentation through contrast enhanced torso CT dataset. - Sanne E. Okel, Fons van der Sommen
Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks. - Cristina Oyarzun Laura, Katrin Hartwig, Alexander Distergoft, Tim Hoffmann, Kathrin Scheckenbach, Melanie Brüsseler, Stefan Wesarg:
Automatic segmentation of the structures in the nasal cavity and the ethmoidal sinus for the quantification of nasal septal deviations. - Yue Li, Zilong He, Xiangyuan Ma, Weimin Xu, Chanjuan Wen, Hui Zeng, Weixiong Zeng, Zeqi Wu, Genggeng Qin, Weiguo Chen, Yao Lu:
Architectural distortion detection in digital breast tomosynthesis with adaptive receptive field and adaptive convolution kernel shape. - Andrew Elliott, Cole Morgan, Carlo Torres, Caroline Chung
Probabilistic segmentation of small metastatic brain tumors using liquid state machine ensemble. - Hyeonjin Kim, Helen Hong, Dae Chul Jung
Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with signal intensity correction. - Qiang Wang
Fluorescence lifetime imaging endomicroscopy-based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks. - Yin Xi
Assessing reproducibility in magnetic resonance (MR) radiomics features between deep-learning segmented and expert manual segmented data and evaluating their diagnostic performance in pregnant women with suspected placenta accreta spectrum (PAS). - Azad Aminpour, Mehran Ebrahimi
Deep learning-based lesion segmentation in paediatric epilepsy. - Aryan Ghazipour, Benjamin Veasey, Albert Seow, Amir A. Amini:
3D U-net for registration of lung nodules in longitudinal CT scans. - Nikolas Schnellbächer, Haissam Ragab, Hannes Nickisch
Machine-learning-based clinical plaque detection using a synthetic plaque lesion model for coronary CTA. - Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Ludovic Ferrer
Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images. - Mika Yamamuro, Yoshiyuki Asai
How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography. - Apurva Singh
Development of a radiogenomic biomarker for tumor characterization and prognosis in non-small cell lung cancer patients. - Masahiro Oda
Lung infection and normal region segmentation from CT volumes of COVID-19 cases. - Meiqing Pan, Zhenchao Tang, Sirui Fu, Wei Mu, Jie Zhang, Xiaoqun Li, Hui Zhang, Ligong Lu
Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma. - Gopichandh Danala
Applying quantitative image markers to predict clinical measures after aneurysmal subarachnoid hemorrhage. - Jordan D. Fuhrman, Linnea Kremer, Yeqing Zhu
Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans. - Roger Fonolla, Maciej Smyl, Fons van der Sommen
Triplet network for classification of benign and pre-malignant polyps. - Tomoki Uemura, Janne J. Näppi, Chinatsu Watari, Tohru Kamiya, Hiroyuki Yoshida:
Unsupervised survival prediction model from CT images of patients with COVID-19.
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