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ORCIDMedical Imaging: Computer-Aided Diagnosis 2023: San Diego, CA, USA
- Khan M. Iftekharuddin, Weijie Chen:
Medical Imaging 2023: Computer-Aided Diagnosis, San Diego, CA, USA, February 19-23, 2023. SPIE Proceedings 12465, SPIE 2023, ISBN 9781510660359
Spie Medical imaging Awards and Plenary
- Zachary Chase Lipton:
Reliable deep learning in dynamic environments.
Neurology
- Sayantan Kumar, Philip R. O. Payne, Aristeidis Sotiras:
Normative modeling using multimodal variational autoencoders to identify abnormal brain volume deviations in Alzheimer's disease. - Sina Walluscheck, Luca Canalini, Jan Klein, Stefan Heldmann:
Unsupervised learning of healthy anatomy for anomaly detection in brain CT scans. - Xinyang Liu, Erin R. Bonner, Zhifan Jiang, Holger R. Roth, Roger J. Packer, Miriam Bornhorst, Marius George Linguraru:
From adult to pediatric: deep learning-based automatic segmentation of rare pediatric brain tumors. - Kosuke Kita, Takahito Fujimori, Yuki Suzuki, Seiji Okada, Shoji Kido:
Bi-modal network combining convolutional neural network and TabNet, differentiating spinal tumors based on images and clinical risk factors.
Radiomics, Radiogenomics, and Multi-omics
- J. L. Cozzi, Hui Li, Julian Conn Busch, J. Williams, Li Lan, X. Keutgen, Maryellen L. Giger:
Novel integration of radiomics and deep transfer learning for diagnosis of indeterminate thyroid nodules on ultrasound. - Hannah Horng, Despina Kontos, Russell T. Shinohara:
MultiComBat: ComBat harmonization of multiple batch variables. - Alexandra C. Shiffer, Hyun J. Grace Kim, Michael F. McNitt-Gray:
Prediction of recurrence in non-small cell lung cancer (NSCLC) using Gabor and radiomic-feature based models applied to CT image data. - M. Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski, Axel Wismüller:
Classification of schizophrenia using large-scale kernelized Granger causality (lsKGC) and functional MR imaging. - Saba Dadsetan, Marcio Albers, Allison Weinstock, Volker Musahl, Gene Kitamura, Dooman Arefan, Shandong Wu:
Anterior cruciate ligament classification in knee MRI using automated pseudo-masking.
COVID-19
- Chunrui Zou, Walter C. Mankowski, Lauren Pantalone, Shefali Setia Verma, Eduardo Jose Mortani Barbosa Jr., Tessa S. Cook, Peter B. Noël, Erica L. Carpenter, Jeffrey C. Thompson, Russell T. Shinohara, Sharyn Katz, Despina Kontos:
A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images. - Ryo Toda, Masahiro Oda
, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori:
Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks. - Catalin I. Fetita, Antoine Didier, Jean Richeux, Christian Tulvan, Jean-François Bernaudin, Pierre-Yves Brillet, Aurélien Justet:
Linking CT and SPECT based analysis for quantitative follow-up of vascular perfusion defects in COVID-19. - Mena Shenouda, Aditi Kaveti, Isabella Flerlage, Jayashree Kalpathy-Cramer, Maryellen L. Giger, Samuel G. Armato III:
Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. - Mohammadreza Zandehshahvar, Marly van Assen, Eun Young Kim, Yashar Kiarashi, Vikranth Keerthipati, Arthur E. Stillman, Peter Filev, Amir H. Davarpanah, Eugene A. Berkowitz, Stefan Tigges, Scott J. Lee, Brianna L. Vey, Carlo Nicola De Cecco, Ali Adibi:
Bayesian neural networks for severity assessment of COVID-19 pneumonia from chest x-ray using a multi-reader and binational dataset. - Dabbara Keshava Chowdari, Nunna Radhasyam, Anabik Pal, Angshuman Paul:
Federated learning using multi-institutional data for generalizable chest x-ray diagnosis.
Breast
- Xuan Liu, Yinhao Ren, Zisheng Liang, Lars J. Grimm, Jun Ge, Joseph Y. Lo:
Multi-view DBT grid-attention detection framework. - Md Belayat Hossain, Juhun Lee:
Developing a task-oriented deep convolutional neural network application towards estimating near-term breast cancer risk: preliminary work. - Sjoerd A. M. Tunissen, Andrea Motta, Franziska Mauter, Eloy García, Oliver Díaz, John M. Boone, Ioannis Sechopoulos, Marco Caballo:
End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms. - Jing Wang, Boran Zhou, Mylin A. Torres, Xiaofeng Yang, Tian Liu:
Quantitative ultrasound radiomics for monitoring radiation-induced toxicity following breast radiotherapy.
Novel Applications
- Gaoya Shen, Yaping Wang, Xiujuan Geng:
Identification of autism spectrum disorder based on MEWISPool and multi-modal learning. - James Huang, Quyen N. Do, Maysam Shahedi, Yin Xi, Matthew A. Lewis, Christina L. Herrera, David Owen, Catherine Y. Spong, Ananth J. Madhuranthakam, Diane M. Twickler, Baowei Fei:
Deep-learning-based automatic segmentation of the placenta and uterine cavity on prenatal MR images. - Akshaya Anand, Jianfei Liu, Thomas C. Shen, W. Marston Linehan, Peter A. Pinto, Ronald M. Summers:
Automated classification of intravenous contrast enhancement phase of CT scans using residual networks. - Yoon Jo Kim, Min Jin Lee, Helen Hong, Sung Il Hwang:
Deep regression model with ordinal and triplet loss for the prediction of prostate cancer aggressiveness in multi-parametric MR images. - Ahmed G. Temtam, Liangsuo Ma, F. Gerard Moeller, Md. Shibly Sadique, Khan M. Iftekharuddin:
Opioid use disorder prediction using machine learning of fMRI data.
AI: Joint Session with Conferences 12465 AND 12467
- Lin Guo, Kunlei Hong, Ziqi Zhang, Bin Zheng, Stefan Jaeger, Jordan D. Fuhrman, Hui Li, Maryellen L. Giger, Andrei Gabrielian, Alex Rosenthal, Darrell E. Hurt, Ziv Yaniv, Y. M. Fleming Lure:
Assessing an AI-based smart imagery framing and truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest x-ray images for development of deep learning models. - Thom Scheeve, Nikoo Dehghani, Quirine E. W. van der Zander, Ayla Thijssen, Ramon-Michel Schreuder, Ad A. M. Masclee, Erik J. Schoon, Fons van der Sommen, Peter H. N. de With:
How does image quality affect computer-aided diagnosis of colorectal polyps? - Chih-Chieh Liu, Qiulin Tang, Liang Cai, Zhou Yu, Jian Zhou:
Automated right coronary artery localizer using deep learning for optimal cardiac phase selection.
Wednesday morning Keynotes
- Dale Webster:
From code to clinic: challenges in translating ML models into real-world products.
Deep Learning I
- Md. Shibly Sadique, Walia Farzana, Ahmed G. Temtam, Khan M. Iftekharuddin:
Class activation mapping and uncertainty estimation in multi-organ segmentation. - Satrajit Chakrabarty, Pamela LaMontagne, Joshua S. Shimony, Daniel S. Marcus, Aristeidis Sotiras:
Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network. - Soumyendu Sekhar Ghosh, Rajat Dhar, Daniel S. Marcus, Aristeidis Sotiras:
Siam-VAE: a hybrid deep learning based anomaly detection framework for automated quality control of head CT scans. - Jennie Karlsson, Ida Arvidsson, Freja Sahlin, Kalle Åström, Niels Christian Overgaard, Kristina Lång, Anders Heyden:
Classification of point-of-care ultrasound in breast imaging using deep learning.
Eye and retina
- Mingzhe Hu, Jing Wang, Jacob F. Wynne, Tian Liu, Xiaofeng Yang:
A vision-GNN framework for retinopathy classification using optical coherence tomography. - Timo Kepp, Julia Andresen, Claus von der Burchard, Johann Roider, Gereon Hüttmann, Heinz Handels:
Shape-based segmentation of retinal layers and fluids in OCT image data. - Joshua Niemeijer, Jan Ehrhardt, Timo Kepp, Jörg P. Schäfer, Heinz Handels:
Overcoming the sensor delta for semantic segmentation in OCT images. - Alexander Shieh, Tejas Sudharshan Mathai, Jianfei Liu, Angshuman Paul, Ronald M. Summers:
Correcting class imbalances with self-training for improved universal lesion detection and tagging. - Anneke Annassia Putri Siswadi, Stéphanie Bricq, Fabrice Mériaudeau:
Multi-label ocular abnormalities detection with semantic dictionary learning.
Segmentation
- Sudeep Katakol, Zhangxing Bian, Yanglong Lu, Greg Spahlinger, Charles R. Hatt, Nicholas S. Burris:
Fully automated aortic measurements via CNN-based joint segmentation and localization. - Souvick Mukherjee, Cameron Duic, Tharindu De Silva, Tiarnan D. Keenan, Alisa T. Thavikulwat, Emily Y. Chew, Catherine Cukras:
Drusen segmentation in color fundus photographs for drusenoid pigment epithelial detachment patients based on ground-truth derived from SD-OCTs. - Francesco Marzola, Kristen M. Meiburger, Filippo Molinari, Massimo Salvi:
Can multiple segmentation methods enhance deep learning networks generalization? A novel hybrid learning paradigm. - Manas Kumar Nag, Jianfei Liu, Seung Yeon Shin, Benjamin Hou, Liangchen Liu, Perry J. Pickhardt, Jung-Min Lee, Ronald M. Summers:
Improved ascites segmentation with bladder identification using anatomical location residual U-Net. - Shuyue Guan, Ravi K. Samala, Arian Arab, Weijie Chen:
MISS-tool: medical image segmentation synthesis tool to emulate segmentation errors. - Hidenobu Suzuki, Yoshiki Kawata, Toshihiko Sugiura, Nobuhiro Tanabe, Yuji Matsumoto, Takaaki Tsuchida, Masahiko Kusumoto, Kazuyoshi Marumo, Masahiro Kaneko, Noboru Niki:
Automated detection method of thoracic aorta calcification from non-contrast CT images using mediastinal anatomical label map.
Head, neck, and musculoskeletal
- Moinak Bhattacharya, Prateek Prasanna:
Audio-visual feature fusion for improved thoracic disease classification. - Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer:
Unsupervised anomaly detection of paranasal anomalies in the maxillary sinus. - Tricia Chinnery, Pencilla Lang, Anthony Nichols, Sarah A. Mattonen:
Predicting the need for a replan in oropharyngeal cancer: a radiomic, clinical, and dosimetric model. - Eren Bora Yilmaz, Tobias Fricke, Julian Laue, Constanze Polzer, Sam Sedaghat, Jan-Bernd Hövener, Claus-Christian Glüer, Carsten Meyer:
Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures.
Deep Learning II
- Tejas Sudharshan Mathai, Sungwon Lee, Thomas C. Shen, Zhiyong Lu, Ronald M. Summers:
Universal lymph node detection in multiparametric MRI with selective augmentation. - Dallas K. Tada, Pangyu Teng, Michael F. McNitt-Gray, Grace H. Kim, Matthew S. Brown, Jonathan G. Goldin, Kalyani Vyapari, Ashley Banola:
3D patch-based CNN for fissure segmentation on CT images to quantitatively assess fissure integrity and evaluate emphysema patients for endobronchial valve treatment. - Nikoo Dehghani, Thom Scheeve, T. G. W. Boers, Quirine E. W. van der Zander, Ayla Thijssen, Ramon-Michel Schreuder, Ad A. M. Masclee, Erik J. Schoon, Fons van der Sommen, Peter H. N. de With:
Effect of domain-specific self-supervised pretraining on predictive uncertainty for colorectal polyp characterization. - Alexis Burgon, Nicholas Petrick, Berkman Sahiner, Gene Pennello, Ravi K. Samala:
Decision region analysis to deconstruct the subgroup influence on AI/ML predictions. - Christiaan G. A. Viviers, M. M. Amaan Valiuddin, Peter H. N. de With, Fons van der Sommen:
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data.
Lung and Abdomen
- Saman Sotoudeh-Paima, Ehsan Samei, Ehsan Abadi:
CT-HARMONICA: physics-based CT harmonization for reliable lung density quantification. - Jun Wang, Jihong Sun, Xiaoyin Xu, Min Zhang:
Gate-SBNet: gate semantic boundary network for colorectal polyp segmentation. - Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha:
RUPNet: residual upsampling network for real-time polyp segmentation. - Rohini Gaikar, Azar Azad, Nicola Schieda, Eranga Ukwatta:
Fully automated cascaded approach for renal mass detection on T2 weighted MRI images. - Mathias Ramm Haugland, Hemin Ali Qadir, Ilangko Balasingham:
Deep learning for improved polyp detection from synthetic narrow-band imaging. - Thomas Zeng, Elias Furst, Yiyang Wang, Roselyne Tchoua, Jacob Furst, Daniela Raicu:
No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes.
Poster Session
- Carolus H. J. Kusters, T. G. W. Boers, Jelmer B. Jukema, Martijn R. Jong, Kiki N. Fockens, Albert J. de Groof, Jacques J. Bergman, Fons van der Sommen, Peter H. N. de With:
Real-time Barrett's neoplasia characterization in NBI videos using an int8-based quantized neural network. - Keita Otani, Daiki Nishigaki, Taro Hatsutani, Takeshi Takamoto, Yuki Suzuki, Shoji Kido, Noriyuki Tomiyama:
Automatic characterization of liver tumors from multi-phase CT images. - Jin Yang, Daniel S. Marcus, Aristeidis Sotiras:
Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations. - Seung Yeon Shin, Thomas C. Shen, Stephen A. Wank, Ronald M. Summers:
Improving small lesion segmentation in CT scans using intensity distribution supervision: application to small bowel carcinoid tumor. - Siyuan Wei, Youngwon Choi, M. Wasil Wahi-Anwar, Liza Shrestha, Koon-Pong Wong, Matthew S. Brown:
Catheter segmentation in chest x-ray: improving imbalanced segmentation with a class frequency weighted loss function. - Gabriel Melendez-Corres, Youngwon Choi, M. Wasil Wahi-Anwar, Heidi Coy, Steven S. Raman, Matthew S. Brown:
Machine reasoning for segmentation of the kidneys on CT images: improving CNN performance by incorporating anatomical knowledge in post-processing. - Behnaz Elhaminia, Alexandra Gilbert, Alejandro F. Frangi, Andrew F. Scarsbrook, John Lilley, Ane Appelt, Ali Gooya:
Deep learning with visual explanation for radiotherapy-induced toxicity prediction. - Daniel Capellán-Martín, Juan J. Gómez-Valverde, Ramon Sánchez-Jacob, David Bermejo-Peláez, Lara García-Delgado, Elisa López-Varela, María J. Ledesma-Carbayo:
Deep learning-based lung segmentation and automatic regional template in chest x-ray images for pediatric tuberculosis. - Antonio Tejero-de-Pablos, Hiroaki Yamane, Yusuke Kurose, Junichi Iho, Youji Tokunaga, Makoto Horie, Keisuke Nishizawa, Yusaku Hayashi, Yasushi Koyama, Tatsuya Harada:
Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall. - Ao Ran, Chengjin Yu, Huafeng Liu:
A deep learning method for localizing the origin of ventricular tachycardia using 12-lead ECG. - Akihiro Taguchi, Samantha E. Seymour, Ciprian N. Ionita, Kurt Schultz, Ryo Shiroishi:
Prediction of hematoma expansion using a random forest model with clinical data of patients with intracerebral hemorrhage. - Viet-Huan Le, Tran Nguyen Tuan Minh, Quang-Hien Kha, Nguyen-Quoc-Khanh Le:
An MRI-based radiomics signatures for overall survival prediction of gliomas patients. - Stijn Vandewiele, Jonas De Vylder, Bart Diricx, Sandra Edward, Tom Kimpe:
Open-source tool for model performance analysis for subpopulations. - Edoardo Coppola, Damiano Ferrari, Mattia Savardi, Alberto Signoroni:
Explainable AI for COVID-19 prognosis from early chest x-ray and clinical data in the context of the COVID-CXR international hackathon. - Yuheng Li, Jing Wang, Mingzhe Hu, Pretesh Patel, Hui Mao, Tian Liu, Xiaofeng Yang:
Prostate Gleason score prediction via MRI using capsule network. - Tianyi Wang, Mingkai Li, Zebin Chen, Xi Chen, Haimei Chen, Bin Wu, Xiaoying Wu, Yue Li, Yao Lu:
Tensor based multi-modality fusion for prediction of postoperative early recurrence of single hepatocellular carcinoma. - T. Kodama, Hidetaka Arimura, Y. Shirakawa, Kenta Ninomiya, T. Yoshitake, Yoshiyuki Shioyama:
Topological prediction models for relapse of stage I patients with non-small cell lung cancer prior to stereotactic ablative radiotherarpy. - Stepan Romanov, Sacha Howell, Elaine Harkness, Megan Bydder, D. Gareth Evans, Steven Squires, Martin Fergie, Susan M. Astley:
Artificial intelligence for image-based breast cancer risk prediction using attention. - T. G. W. Boers, Carolus H. J. Kusters, Kiki N. Fockens, Jelmer B. Jukema, Martijn R. Jong, Jeroen de Groof, Jacques J. Bergman, Fons van der Sommen, Peter H. N. de With:
Barrett's lesion detection using a minimal integer-based neural network for embedded systems integration. - Youngjin Yoo, Gengyan Zhao, Andreea Sandu, Thomas J. Re, Jyotipriya Das, Hesheng Wang, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, Dorin Comaniciu:
The importance of data domain on self-supervised learning for brain metastasis detection and segmentation. - Chelsea E. Harris, Sokratis Makrogiannis:
Breast mass characterization using sparse approximations of patch-sampled deep features. - Raissa Souza, Emma A. M. Stanley, Milton Camacho, Matthias Wilms, Nils D. Forkert:
An analysis of intensity harmonization techniques for Parkinson's multi-site MRI datasets. - Apurva Singh, Hannah Horng, Leonid Roshkovan, Sharyn I. Katz, Despina Kontos, Jeffrey C. Thompson:
Radiomics analysis to diagnose tumor invasiveness of pulmonary sub-solid nodules from longitudinal pre-surgical CT scans. - Shanshan Cai, John Mai, Winn Hong, Scott Fraser, Francesco Cutrale:
Multiplexed diffused optical imaging generative adversarial network (mDOI-GAN) for sub-surface 3D imaging of tissues. - Katherine Aubert, Catherine Huber, Jacob Furst, Daniela Stan Raicu, Roselyne Tchoua:
Iterative K-means clustering for disease subtype discovery. - Martha Rebeca Canales-Fiscal, José Gerardo Tamez-Peña:
Glaucoma classification using a morphological-convolutional neural network trained with extreme learning machine. - Sanjana Mudduluru, Sai Kiran Reddy Maryada, William Booker, Dean F. Hougen, Bin Zheng:
Improving medical image segmentation and classification using a novel joint deep learning model. - Juhun Lee, Robert M. Nishikawa:
Developing a breast lesion simulator and remover in mammograms using Cycle-GAN: focusing on its impacts on a computer aided detection algorithm. - Abdelhamed Mohamed, Fabrice Mériaudeau:
NesT UNet: pure transformer segmentation network with an application for automatic cardiac myocardial infarction evaluation. - Terese Hellström, Christiaan G. A. Viviers, Mark Ramaekers, Nick Tasios, Joost Nederend, Misha Luyer, Peter H. N. de With, Fons van der Sommen:
Clinical segmentation for improved pancreatic ductal adenocarcinoma detection and segmentation. - Alejandra Moreno, Andrea Rueda, Fabio Martínez:
A volumetric multi-head attention strategy for lung nodule classification in CT. - Aman Kushwaha, Rami F. Mourad, Kevin Heist, Dariya I. Malyarenko, Heang-Ping Chan, Thomas L. Chenevert, Lubomir M. Hadjiiski:
Segmentation of mouse tibia on MRI using deep learning U-Net models. - Ziyu Su, Sandhya Kumar, Thomas E. Tavolara, Metin N. Gurcan, Scott Segal, M. Khalid Khan Niazi:
Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models. - Yongfeng Gao, Shaojie Chang, Marc Jason Pomeroy, Lihong Li, Zhengrong Liang:
Using virtual monoenergetic images in Karhunen-Loève domain to differentiate lesion pathology. - Daniel Liang, David Liang, Alice Wei, Licheng Ryan Kuo, Shaojie Chang, Marc Jason Pomeroy, Yongfeng Gao:
An investigation on energy spectral information of computed tomography for machine learning in lesion classification. - Chelsea A. S. Dunning, Prabhakar Shantha Rajiah, Scott S. Hsieh, Andrea Esquivel, Mariana Yalon, Nikkole M. Weber, Hao Gong, Joel G. Fletcher, Cynthia H. McCollough, Shuai Leng:
Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images. - Lara Dular, Gregor Brecl-Jakob, Lina Savsek, Jozef Magdic, Ziga Spiclin:
Predicting future multiple sclerosis disease progression from MR scans. - Xiangyuan Ma, Zilong He, Yue Li, Weixiong Zeng, Jiawei Pan, Jialing Liu, Weimin Xu, Zeyuan Xu, Sina Wang, Chanjuan Wen, Hui Zeng, Jiefeng Wu, Zhaodong Zeng, Weiguo Chen, Yao Lu:
Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis. - Aldir Sousa, Marcelo Toledo, José Eduardo Krieger, Marco Antonio Gutierrez:
Automatic segmentation of stroke lesions in T1-weighted magnetic resonance images with convolutional neural networks. - João Vitor Alcantara, Joany Rodrigues, Paulo Rogério Julio, Simone Appenzeller, Letícia Rittner:
Volumetric corpus callosum segmentation integrated to inCCsight software for supporting DTI-based studies. - Linnea Kremer, Boris Fosso, Lucy Groothuis, Arlene Chapman, Samuel G. Armato III:
Radiomics-based classification of autosomal dominant polycystic kidney disease (ADPKD) Mayo imaging classification (MIC) and the effect of gray-level discretization. - John M. Hoffman, Frédéric Noo, Michael F. McNitt-Gray:
An analytic, physics-based approach to scoring emphysema in lung CT patients. - Heeyoung Jeong, Hyeonjin Kim, Helen Hong, Dae Chul Jung, Kidon Chang, Koon Ho Rha:
Renal parenchyma segmentation based on a cascaded self-adaptive framework with local context-aware mix-up regularization in abdominal MR images. - Nathan Hadjiyski, M. Ali Vosoughi, Axel Wismüller:
Cross modal global local representation learning from radiology reports and x-ray chest images. - Jonathan Clever, Lubomir M. Hadjiiski, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Kenny H. Cha, Ravi Samala, Chuan Zhou:
Bladder cancer segmentation using U-Net-based deep-learning. - Di Sun, Lubomir M. Hadjiiski, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Ajjai Alva, Kenny H. Cha, Ravi K. Samala, Chuan Zhou:
Bladder cancer treatment response assessment in CT urography by using deep-learning and radiomics. - Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori:
Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-mixer. - Ipsa Yadav, Vaibhav Bahel, François De Guio, Juhi Desai, Nikhil Gupta, Latha Poonamallee:
A 2D U-NET combined model based on lesion size for automated stroke lesion segmentation. - Long Gao, Chang Liu, Dooman Arefan, Ashok Panigrahy, Margarita L. Zuley, Shandong Wu:
Medical knowledge-guided deep learning for mammographic breast density classification. - Titinunt Kitrungrotsakul, Qingqing Chen, Huitao Wu, Preeyanuch Srichola, Hongjie Hu, Wenchao Zhu, Chao Chen, Fangyi Xu, Yong Zhou, Lanfen Lin, Ruofeng Tong, Jingsong Li, Yen-Wei Chen:
A hybrid model of deep learning features and clinical features for severe cases predication of COVID-19. - Jared Frazier, Tejas Sudharshan Mathai, Jianfei Liu, Angshuman Paul, Ronald M. Summers:
3D universal lesion detection and tagging in CT with self-training. - Chisako Muramatsu, Koki Sakai, Yuta Seino, Ryo Takahashi, Tatsuro Hayashi, Wataru Nishiyama, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita:
Recognition of tooth numbers and conditions on dental panoramic radiographs for assisting dental chart filing. - Rain Tarango, Lubomir M. Hadjiiski, Ajjai Alva, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Kenny H. Cha, Ravi K. Samala, Alon Z. Weizer, Chuan Zhou, Monika Joshi, Yousef Zakharia:
Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning.
Digital poster Session
- Jiabao Jin, Yu Wu, Gang Ding, Ying Ma, Hui-Qing Zeng, Sunkui Ke, Xiangxing Chen, Miao Wang, Yinran Chen, Xiongbiao Luo:
Towards a relation extractor U-shaped network for accurate pulmonary vessel segmentation in CT images. - Ying Ma, Jiabao Jin, Guanping Xu, Ao Wang, Yinran Chen, Weijie Ouyang, Xiang Lin, Zuguo Liu, Xiongbiao Luo:
Densely encoded attention networks for accurate retinal layers segmentation in optical coherence tomography. - Mohsen Soltanpour, Muhammad Yousefnezhad, Russ Greiner, Pierre Boulanger, Brian Buck:
Using temporal GAN to translate the current CTP scan to follow-up MRI, for predicting final acute ischemic stroke lesions.
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