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ORCIDMedical Imaging: Computer-Aided Diagnosis 2022: San Diego, CA, USA / online
- Karen Drukker, Khan M. Iftekharuddin:
Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, CA, USA, February 20-24, 2022 / online, March 21-27, 2022. SPIE Proceedings 12033, SPIE 2022, ISBN 9781510649415
Novel Applications
- Shaojie Chang, Yongfeng Gao, Marc Jason Pomeroy, Siming Lu, Zhengrong Liang:
A feasibility study of computer-aided diagnosis with DECT Bayesian reconstruction for polyp classification. - Xiaohong W. Gao
, Stephen Taylor, Wei Pang, Xin Lu, Barbara Braden:
Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. - Kai Jiang, Masahiro Oda, Hironari Shiwaku, Masashi Misawa, Kensaku Mori:
Real-time esophagus achalasia detection method for esophagoscopy assistance.
COVID-19
- Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima, Rafael B. Fricks, Maciej A. Mazurowski, William Paul Segars, Ehsan Samei, Joseph Y. Lo:
Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography. - Idil Aytekin, Onat Dalmaz, Haydar Ankishan, Emine Ulku Saritas, Ulas Bagci, Tolga Çukur, Haydar Celik:
Detecting COVID-19 from respiratory sound recordings with transformers. - Aishik Konwer, Prateek Prasanna:
Clinical outcome prediction in COVID-19 using self-supervised vision transformer representations. - Benjamin Frey, Lingyi Zhao, Tiffany Clair Fong, Muyinatu A. Lediju Bell:
Multi-stage investigation of deep neural networks for COVID-19 B-line feature detection in simulated and in vivo ultrasound images. - Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori:
Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes. - Catalin I. Fetita, Mathilde Maury, Aurélien Justet, Juliette Dindart, Jean Richeux, Lucile Sese, Nicolas Aide, Thomas Gille, Hilario Nunes, Jean-François Bernaudin, Pierre-Yves Brillet:
Multiparameter analysis of vascular remodeling in post-acute sequelae of COVID-19.
Breast I
- Juhun Lee, Robert M. Nishikawa:
Analyzing GAN artifacts for simulating mammograms: application towards finding mammographically-occult cancer. - Jun Luo, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, Shandong Wu:
Deep curriculum learning in task space for multi-class based mammography diagnosis. - Belayat Hossain, Robert M. Nishikawa, Juhun Lee:
Improving lesion detection algorithm in digital breast tomosynthesis leveraging ensemble cross-validation models with multi-depth levels. - Warid Islam, Gopichandh Danala, Huong Pham, Bin Zheng:
Improving the performance of computer-aided classification of breast lesions using a new feature fusion method. - Simona Rabinovici-Cohen, Tal Tlusty, Xosé M. Fernández, Beatriz Grandal Rejo:
Early prediction of metastasis in women with locally advanced breast cancer.
Translation of CAD-AI Methods to Clinical Practice: are we there yet?: Joint Session with Conferences 12033 and 12035
- Di Sun, Lubomir M. Hadjiiski, Rohan Garje, Yousef Zakharia, Lauren Pomerantz, Monika Joshi, Ajjai Alva, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Kenny H. Cha, Galina Kirova-Nedyalkova, Matthew S. Davenport, Prasad R. Shankar, Isaac R. Francis, Kimberly Shampain, Nathaniel Meyer, Daniel Barkmeier, Sean Woolen, Phillip L. Palmbos, Alon Z. Weizer, Ravi K. Samala, Chuan Zhou, Martha M. Matuszak:
Effect of computerized decision support on diagnostic accuracy and intra-observer variability in multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography.
Deep Learning I
- Amir Reza Sadri, Thomas DeSilvio, Prathyush Chirra, Andrei S. Purysko, Rajmohan Paspulati, Kenneth Friedman, Smitha S. Krishnamurthi, David Liska, Sharon L. Stein, Satish E. Viswanath:
Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI.
Translation of CAD-AI Methods to Clinical Practice: are we there yet?: Joint Session with Conferences 12033 and 12035
- Anshul Ratnaparkhi, Bilwaj Gaonkar, David Zarrin, Ien Li, Kirstin Cook, Azim Laiwalla, Bayard Wilson, Mark Attiah, Christine S. Ahn, Diane Villaroman, Bryan Yoo, Banafsheh Salehi, Joel Beckett, Luke Macyszyn:
Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets.
Deep Learning I
- Ghada Zamzmi, Tochi Oguguo, Sivaramakrishnan Rajaraman, Sameer K. Antani:
Open-world active learning for echocardiography view classification. - Sai Kiran Reddy Maryada, William Booker, Gopichandh Danala, Catherine An Ha, Sanjana Mudduluru, Dean F. Hougen, Bin Zheng:
Applying a novel two-stage deep-learning model to improve accuracy in detecting retinal fundus images. - Ravi K. Samala, Berkman Sahiner, Gene Pennello, Kenny H. Cha, Mohammad Mehdi Farhangi, Nicholas Petrick:
Deciphering deep ensembles for lung nodule analysis. - Samuel Robertson, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert:
Stroke lesion localization in 3D MRI datasets with deep reinforcement learning. - Álvaro García-Faura, Dejan Stepec, Tomaz Martincic, Danijel Skocaj:
Segmentation of multiple myeloma plasma cells in microscopy images with noisy labels.
Detection
- Axel Wismüller, M. Ali Vosoughi, Adora M. DSouza, Anas Z. Abidin:
Exploring directed network connectivity in complex systems using large-scale augmented Granger causality (lsAGC). - Indrani Bhattacharya, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Jeffrey B. Wang, Christian Kunder, Pejman Ghanouni, Geoffrey A. Sonn, Mirabela Rusu:
Integrating zonal priors and pathomic MRI biomarkers for improved aggressive prostate cancer detection on MRI. - Kento Nishihira, Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Yuuki Kobari, Atsushi Ikeda, Noboru Niki:
Renal tumor analysis using multi-phase abdominal CT images. - Omer Zucker Shmueli, Chen Solomon, Noam Ben-Eliezer, Hayit Greenspan:
Deep learning based multiple sclerosis lesion detection utilizing synthetic data generation and soft attention mechanism.
Breast II
- Natalie M. Baughan, Lindsay Douglas, Maya Ballard, Esther Seoyeon Lee, Alexandra Edwards, Li Lan, Hui Li, Maryellen L. Giger:
Association between DCE MRI background parenchymal enhancement and mammographic texture features. - Heather M. Whitney, Yu Ji, Hui Li, Peifang Liu, Maryellen L. Giger:
Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers. - Shannon Doyle, Francesco Dal Canton, Jelle Wesseling, Clara I. Sánchez, Jonas Teuwen:
Mammary duct detection using self-supervised encoders. - Yen Nhi Truong Vu, Brent Mombourquette, Thomas Paul Matthews, Jason Su, Sadanand Singh:
WRDet: a breast cancer detector for full-field digital mammograms. - Yue Li, Zilong He, Xiangyuan Ma, Weixiong Zeng, Jialing Liu, Weimin Xu, Zeyuan Xu, Sina Wang, Chanjuan Wen, Hui Zeng, Jiefang Wu, Weiguo Chen, Yao Lu:
Computer-aided detection for architectural distortion: a comparison of digital breast tomosynthesis and digital mammography.
Deep Learning II
- Zhiyang Zheng, Yi Su, Kewei Chen, David Weidman, Teresa Wu, Ben Lo, Fleming Lure, Jing Li:
Addressing imaging accessibility by cross-modality transfer learning. - Degan Hao, Dooman Arefan, Shandong Wu:
Incorporate radiograph-reading behavior and knowledge into deep reinforcement learning for lesion localization. - Ansh Roge, Amogh Hiremath, Michael Sobota, Sree Harsha Tirumani, Leonardo Kayat Bittencourt, Justin Ream, Ryan Ward, Halimat Olaniyan, Sadhna Verma, Andrei S. Purysko, Anant Madabhushi, Rakesh Shiradkar:
Evaluating the sensitivity of deep learning to inter-reader variations in lesion delineations on bi-parametric MRI in identifying clinically significant prostate cancer. - Yongjian Yu, Jue Wang:
Categorization of tumor-derived cells from lung cancer with compact deep learning. - Mina Rezaei, Janne J. Näppi, Bernd Bischl, Hiroyuki Yoshida:
Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images. - Anees Kazi, Viktoria Markova, Prabhat R. Kondamadugula, Beiyan Liu, Ahmed Adly, Shahrooz Faghihroohi, Nassir Navab:
DG-GRU: dynamic graph based gated recurrent unit for age and gender prediction using brain imaging.
Neurology
- David DeVries, Frank Lagerwaard, Jaap Zindler, Timothy P. C. Yeung, George Rodrigues, George Hajdok, Aaron D. Ward:
Effects of feature type and multiple scanners on brain metastasis stereotactic radiosurgery outcome prediction. - Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, Alexander Schlaefer:
Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction. - Emma A. M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert:
A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder. - Abdullah Thabit, Shenpeng Li, Rob Williams, Victor L. Villemagne, Christopher C. Rowe, Vincent Doré, Pierrick Bourgeat:
PET image harmonization using smoothing-CycleGAN. - Alejandro Gutierrez, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert:
Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.
Head and neck, musculoskeletal
- Tricia Chinnery, Pencilla Lang, Anthony Nichols, Sarah A. Mattonen:
A CT-based radiomics model for predicting feeding tube insertion in oropharyngeal cancer. - David Zarrin, Anshul Ratnaparkhi, Bayard Wilson, Kirstin Cook, Ien Li, Azim Laiwalla, Mark Attiah, Joel Beckett, Bilwaj Gaonkar, Luke Macyszyn:
A deep network ensemble for segmentation of cervical spinal cord and neural foramina. - Shinji Nakazawa, Changhee Han, Joe Hasei, Ryuichi Nakahara, Toshifumi Ozaki:
BAPGAN: GAN-based bone age progression of femur and phalange x-ray images. - Larissa C. Schudlo, Yiting Xie, Kirstin Small, Benedikt Graf:
A novel CNN+LSTM approach to classify frontal chest x-rays for spine fractures. - Fidan Mammadli, Fons van der Sommen, Tim Boers, Joost van der Putten, Kiki N. Fockens, Jelmer B. Jukema, Martijn R. Jong, Jacques J. G. H. M. Bergman, Peter H. N. de With:
Efficient endoscopic frame informativeness assessment by reusing the encoder of the primary CAD task.
Radiomics, Radiogenomics, Multi-omics
- Can Cui, Zuhayr Asad, William F. Dean, Isabelle T. Smith, Christopher Madden, Shunxing Bao, Bennett A. Landman, Joseph T. Roland, Lori A. Coburn, Keith T. Wilson, Jeffrey P. Zwerner, Shilin Zhao, Lee E. Wheless, Yuankai Huo:
Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data. - Walia Farzana, Zeina A. Shboul, Ahmed G. Temtam, Khan M. Iftekharuddin:
Uncertainty estimation in classification of MGMT using radiogenomics for glioblastoma patients. - Hannah Horng, Apurva Singh, Bardia Yousefi, Eric A. Cohen, Babak Haghighi, Sharyn Katz, Peter B. Noël, Russell T. Shinohara, Despina Kontos:
Iterative ComBat methods for harmonization of radiomic features. - Marco Caballo, Wendelien B. G. Sanderink, Luyi Han, Yuan Gao, Alexandra Athanasiou, Ritse M. Mann:
4D radiomics in dynamic contrast-enhanced MRI: prediction of pathological complete response and systemic recurrence in triple-negative breast cancer. - Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Issei Imoto, Yasutaka Nakano, Masahiko Kusumoto, Masahiro Kaneko, Noboru Niki:
Visualization and unsupervised clustering of emphysema progression using t-SNE analysis of longitudinal CT images and SNPs.
Poster Session
- Ka'Toria N. Leitch, Maysam Shahedi, James D. Dormer, Quyen N. Do, Yin Xi, Matthew A. Lewis, Christina L. Herrera, Catherine Y. Spong, Ananth J. Madhuranthakam, Diane M. Twickler, Baowei Fei:
Placenta accreta spectrum and hysterectomy prediction using MRI radiomic features. - Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Masahiro Oda, Kensaku Mori:
Multi-class prediction for improving intestine segmentation on non-fecal-tagged CT volume. - Huong Pham, Meredith A. Jones, Tiancheng Gai, Warid Islam, Gopichandh Danala, Javier A. Jo, Bin Zheng:
Identifying an optimal machine learning generated image marker to predict survival of gastric cancer patients. - Ralph Saber, David Henault, Eugene Vorontsov, Emmanuel Montagnon, An Tang, Simon Turcotte, Samuel Kadoury:
Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker. - Alvaro Fernandez-Quilez, Omer Parvez, Trygve Eftestøl, Svein Reidar Kjosavik, Ketil Oppedal:
Improving prostate cancer triage with GAN-based synthetically generated prostate ADC MRI. - Alvaro Fernandez-Quilez, Habib Ullah, Trygve Eftestøl, Svein Reidar Kjosavik, Ketil Oppedal:
One class to rule them all: detection and classification of prostate tumors presence in bi-parametric MRI based on auto-encoders. - Farina Kock, Grzegorz Chlebus, Felix Thielke, Andrea Schenk, Hans Meine:
Hepatic artery segmentation with 3D convolutional neural networks. - Koen C. Kusters, Thom Scheeve, Nikoo Dehghani, Quirine E. W. van der Zander, Ramon-Michel Schreuder, Ad A. M. Masclee, Erik J. Schoon, Fons van der Sommen, Peter H. N. de With:
Colorectal polyp classification using confidence-calibrated convolutional neural networks. - Sven Kuckertz, Jan Klein, Christiane Engel, Benjamin Geisler, Stefan Kraß, Stefan Heldmann:
Fully automated longitudinal tracking and in-depth analysis of the entire tumor burden: unlocking the complexity. - Alvaro Fernandez-Quilez, Trygve Eftestøl, Svein Reidar Kjosavik, Ketil Oppedal:
Learning to triage by learning to reconstruct: a generative self-supervised approach for prostate cancer based on axial T2w MRI. - Chisako Muramatsu, Mikinao Oiwa, Tomonori Kawasaki, Hiroshi Fujita:
Intrinsic subtype classification of breast lesions on mammograms by contrastive learning. - Jennie Karlsson, Jennifer Ramkull, Ida Arvidsson, Anders Heyden, Kalle Åström, Niels Christian Overgaard, Kristina Lång:
Machine learning algorithm for classification of breast ultrasound images. - Hui Meng, Qingfeng Li, Xuefeng Liu, Yong Wang, Jianwei Niu:
DBNet: a dual-branch network for breast cancer classification in ultrasound images. - David Bermejo-Peláez, Raúl San José Estépar, M. Fernández-Velilla, Carmelo Palacios Miras, Guillermo Gallardo Madueño, M. Benegas, Miguel A. Luengo-Oroz, J. Sellarés, M. Sánchez, Gorka Bastarrika, German R. Peces-Barba, Luis Miguel Seijo Maceiras, María J. Ledesma-Carbayo:
Deep-learning characterization and quantification of COVID-19 pneumonia lesions from chest CT images. - Jianfei Liu, Joanne Li, Amday Wolde, Catherine Cukras, Johnny Tam:
Hybrid transformer for lesion segmentation on adaptive optics retinal images. - Hoda Kheradfallah, Janarthanam Jothi Balaji, Varadharajan Jayakumar, Mohammed Abdul Rasheed, Vasudevan Lakshminarayanan:
Annotation and segmentation of diabetic retinopathy lesions: an explainable AI application. - Suhev Shakya, Mariana Vasquez, Yiyang Wang, Roselyne Tchoua, Jacob Furst, Daniela Raicu:
Human-in-the-loop deep learning retinal image classification with customized loss function. - Anna Breger, Felix Goldbach, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler:
Blood vessel segmentation in en-face OCTA images: a frequency based method. - Yabo Fu, Yang Lei, Zhen Tian, Tonghe Wang, Xianjin Dai, Jun Zhou, Mark McDonald, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang:
Deep learning-based longitudinal CT registration for anatomy variation assessment during radiotherapy. - Ka'Toria N. Leitch, Martin T. Halicek, Maysam Shahedi, James V. Little, Amy Y. Chen, Baowei Fei:
Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features. - Lin Chai, Yaping Wang, Weiyang Shi, Yu Zhang, Bing Liu, Tianzi Jiang, Lingzhong Fan:
Linked psychopathology-specific factors and individual structural brain abnormalities in schizophrenia. - Xiangjun Chen, Zhaohui Wang, Yuefu Zhan, Faouzi Alaya Cheikh, Mohib Ullah:
Fusion of clinical phenotypic and multi-modal MRI for acute bilirubin encephalopathy classification. - Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Toshihiko Sugiura, Nobuhiro Tanabe, Masahiko Kusumoto, Masahiro Kaneko, Noboru Niki:
Segmentation of aorta and main pulmonary artery of non-contrast CT images using U-Net for chronic thromboembolic pulmonary hypertension: evaluation of robustness to contacts with blood vessels. - Claire Weissman, Lilly Roelofs, Jacob Furst, Daniela Stan Raicu, Roselyne Tchoua:
Similarity-based uncertainty scores for computer-aided diagnosis. - Karem Daiane Marcomini, Diego Armando Cardona Cárdenas, Agma Juci Machado Traina, José Eduardo Krieger, Marco Antonio Gutierrez:
A deep learning approach for COVID-19 screening and localization on chest x-ray images. - Apurva Singh, Florian A. Hölzl, Sharyn Katz, Despina Kontos:
A comparison of feature selection methods for the development of a prognostic radiogenomic biomarker in non-small cell lung cancer patients. - Yifan Wang, Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Aamer Chughtai:
Fusion of multiple deep convolutional neural networks (DCNNs) for improved segmentation of lung nodules in CT images. - Jonathan Burkow, Gregory Holste, Jeffrey Otjen, Francisco Perez, Joseph Junewick, Adam M. Alessio:
Avalanche decision schemes to improve pediatric rib fracture detection. - Qi Qiu, Kai Sun, Jing Zhang, Panpan Liu, Liang Wang, Junting Zhang, Junlin Zhou, Zhenyu Liu, Jie Tian:
Identifying sinus invasion in meningioma patients before surgery with deep learning. - Xiangjun Chen, Zhaohui Wang, Yuefu Zhan, Faouzi Alaya Cheikh, Mohib Ullah:
Interpretable learning approaches in structural MRI: 3D-ResNet fused attention for autism spectrum disorder classification. - Kimberley M. Timmins, Irene C. van der Schaaf, Iris N. Vos, Ynte M. Ruigrok, Birgitta K. Velthuis, Hugo J. Kuijf:
Deep learning with vessel surface meshes for intracranial aneurysm detection. - Petter Minne, Alvaro Fernandez-Quilez, Dag Aarsland, Daniel Ferreira, Eric Westman, Afina W. Lemstra, Mara Ten Kate, Alessandro Padovani, Irene Rektorova, Laura Bonanni, Flavio Nobili, Milica G. Kramberger, John-Paul Taylor, Jakub Hort, Jón Snædal, Frédéric Blanc, Angelo Antonini, Ketil Oppedal:
A study on 3D classical versus GAN-based augmentation for MRI brain image to predict the diagnosis of dementia with Lewy bodies and Alzheimer's disease in a European multi-center study. - Priscilla Cho, Sajal Dash, Aristeidis Tsaris, Hong-Jun Yoon:
Image transformers for classifying acute lymphoblastic leukemia. - Yoon Jo Kim, Jinseo An, Helen Hong:
Deep ensemble models with multiscale lung-focused patches for pneumonia classification on chest x-ray. - Mohammad R. Salmanpour, Mahdi Hosseinzadeh, Azizeh Akbari, Kasra Borazjani, Kasra Mojallal, Dariush Askari, Ghasem Hajianfar, Seyed Masoud Rezaeijo, Mohammad M. Ghaemi, Amir Hossein Nabizadeh, Arman Rahmim:
Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features. - Md. Shibly Sadique, Ahmed G. Temtam, E. Lappinen, Khan M. Iftekharuddin:
Radiomic texture feature descriptor to distinguish recurrent brain tumor from radiation necrosis using multimodal MRI. - Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Ehsan Samei, Joseph Y. Lo:
Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT. - Jing Ni, Qilei Chen, Ping Liu, Yu Cao, Benyuan Liu:
Spotlight scheme: enhancing medical image classification with lesion location information. - Weiguo Cao, Marc Jason Pomeroy, Yongfeng Gao, Perry J. Pickhardt, Almas F. Abbasi, Jela Bandovic, Zhengrong Liang:
A vector representation of local image contrast patterns for lesion classification. - Meredith A. Jones, Huong Pham, Tiancheng Gai, Bin Zheng:
Fusion of handcrafted and deep transfer learning features to improve performance of breast lesion classification. - Daniel C. Elton, Andy Chen, Perry J. Pickhardt, Ronald M. Summers:
Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning. - Yang Lei, Tonghe Wang, Justin Roper, Sibo Tian, Pretesh Patel, Jeffrey D. Bradley, Ashesh B. Jani, Tian Liu, Xiaofeng Yang:
Neurovascular bundles segmentation on MRI via hierarchical object activation network. - Erikson Júlio De Aguiar, Karem D. Marcomini, Felipe A. Quirino, Marco A. Gutierrez, Caetano Traina Jr., Agma J. M. Traina:
Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases. - Yuzhe Lu, Aadarsh Jha, Ruining Deng, Yuankai Huo:
Contrastive learning meets transfer learning: a case study in medical image analysis. - Rachel Madhogarhia, Anahita Fathi Kazerooni, Sherjeel Arif, Jeffrey B. Ware, Ariana M. Familiar, Lorenna Vidal, Sina Bagheri, Hannah Anderson, Debanjan Haldar, Sophie Yagoda, Erin Graves, Michael Spadola, Rachel Yan, Nadia Dahmane, Chiharu Sako, Arastoo Vossough, Phillip B. Storm, Adam C. Resnick, Christos Davatzikos, Ali Nabavizadeh:
Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning. - Young Jae Kim, Sohyun Byun, Chung il Ahn, Sangwook Cho, Kwang Gi Kim:
Automatic polyp detection using SmartEndo-Net based on fusion feature pyramid network with mix-up edges. - Lujia Wang, Zhiyang Zheng, Yi Su, Kewei Chen, David Weidman, Teresa Wu, Ben Lo, Fleming Lure, Jing Li:
Early prediction of the Alzheimer's disease risk using Tau-PET and machine learning. - Youngwon Choi, Marlena Garcia, Steven S. Raman, Dieter R. Enzmann, Matthew S. Brown:
AI-human interactive pipeline with feedback to accelerate medical image annotation. - Huiqiao Xie, Yang Lei, Tonghe Wang, Justin Roper, Jeffrey D. Bradley, Tian Liu, Hui Mao, Xiaofeng Yang:
High-resolution MR imaging using self-supervised parallel network. - Tomoharu Kiyuna, Noriko Motoi, Hiroshi Yoshida, Hidehito Horinouchi, Tatsuya Yoshida, Takashi Kohno, Shun-ichi Watanabe, Yuichiro Ohe, Atsushi Ochiai:
Drug response prediction using deep neural network trained by adaptive resampling of histopathological images. - Samantha E. Seymour, Ryan A. Rava, Dennis Swetz, Andre Montiero, Ammad Baig, Kurt Schultz, Kenneth V. Snyder, Mohammad Waqas, Jason M. Davies, Elad I. Levy, Adnan H. Siddiqui, Ciprian N. Ionita:
Predicting hematoma expansion after spontaneous intracranial hemorrhage through a radiomics based model. - Ipsa Yadav, Marwa Ismail, Volodymyr Statsevych, Virginia B. Hill, Ramon Correa, Manmeet Ahluwalia, Pallavi Tiwari:
A radiomics approach to distinguish non-contrast enhancing tumor from vasogenic edema on multi-parametric pre-treatment MRI scans for glioblastoma tumors. - Linnea Kremer, Natalie Perri, Eliza Sorber, Arlene Chapman, Samuel G. Armato III:
Normalization of MRI signal intensity in polycystic kidney disease and the effect on radiomic features. - Can Cui, Samuel R. Johnson, Cullen P. Moran, Katherine S. Hajdu, Joanna Shechtel, John J. Block, Brian Bingham, David Smith, Leo Y. Luo, Hakmook Kang, Jennifer L. Halpern, Herbert S. Schwartz, Ginger E. Holt, Joshua M. Lawrenz, Benoit M. Dawant:
Multi-modality classification between myxofibrosarcoma and myxoma using radiomics and machine learning models. - Manu Goyal, Junyu Guo, Lauren Hinojosa, Keith Hulsey, Ivan Pedrosa:
Automated kidney segmentation by mask R-CNN in T2-weighted magnetic resonance imaging. - Yiting Xie, Benedikt Graf, Parisa Farzam, Brian Baker, Christine Lamoureux, Arkadiusz Sitek:
Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT. - Shuheng Cao, Ethan Yu, Aidan Clarke, Yongfeng Gao, Lihong Li:
Multi-channel medical image segmentation method in Hessian domain. - Marlin Siebert, Philipp Rostalski:
Performance evaluation of lightweight convolutional neural networks on retinal lesion segmentation. - Marjaneh Taghavi, Monique Maas, Femke C. R. Staal, Regina G. H. Beets-Tan, Sean Benson:
CNN-based tumor progression prediction after thermal ablation with CT imaging.
Lung
- Apurva Singh, Hannah Horng, Leonid Roshkovan, Michelle Hershman, Russell T. Shinohara, Sharyn Katz, Despina Kontos:
Resampling and harmonization for mitigation of heterogeneity in imaging parameters: a comparative study. - Cindy McCabe, Mojtaba Zarei, William Paul Segars, Ehsan Samei, Ehsan Abadi:
Optimization of imaging parameters of an investigational photon-counting CT prototype for lung lesion radiomics. - Jingnan Jia, Marius Staring, Irene Hernández-Girón, Lucia J. M. Kroft, Anne A. Schouffoer, Berend C. Stoel:
Prediction of lung CT scores of systemic sclerosis by cascaded regression neural networks. - Jun Keun Choi, Ehwa Yang, Chin A. Yi, Minsu Park, Jung Won Moon, Jae-Hun Kim:
Can deep learning model undergo the same process as a human radiologist when determining malignancy of pulmonary nodules?
Abdomen
- Seung Yeon Shin, Sungwon Lee, Ronald M. Summers:
A graph-theoretic algorithm for small bowel path tracking in CT scans. - Tejas Sudharshan Mathai, Sungwon Lee, Daniel C. Elton, Thomas C. Shen, Yifan Peng, Zhiyong Lu, Ronald M. Summers:
Lymph node detection in T2 MRI with transformers. - Jayasree Chakraborty, Joshua S. Jolissaint, Tiegong Wang, Kevin C. Soares, Mithat Gonen, Linda M. Pak, Thomas Börner, Richard K. G. Do, Vinod P. Balachandran, Michael I. D'Angelica, Jeffrey A. Drebin, T. Peter Kingham, Alice C. Wei, William R. Jarnagin:
CT radiomics to predict early hepatic recurrence after resection for intrahepatic cholangiocarcinoma. - Tarun Mattikalli, Tejas Sudharshan Mathai, Ronald M. Summers:
Universal lesion detection in CT scans using neural network ensembles. - Gyeong Woo Cheon, So Hyun Nam, Jaepyeong Cha:
Unsupervised optical small bowel ischemia detection in a preclinical model using convolutional variational autoencoders. - Debayan Bhattacharya, Christian Betz, Dennis Eggert, Alexander Schlaefer:
Self-supervised U-Net for segmenting flat and sessile polyps.
Eye, retina
- Michael Udin, Ciprian N. Ionita, Saraswati Pokharel, Umesh Sharma:
Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning. - Souvick Mukherjee, Tharindu De Silva, Gopal Jayakar, Peyton Grisso, Henry E. Wiley, Tiarnan D. Keenan, Alisa T. Thavikulwat, Emily Y. Chew, Catherine Cukras:
Device specific SD-OCT retinal layer segmentation using cycle-generative adversarial networks in patients with AMD. - Tharindu De Silva, Kristina Heß, Cameron Duic, Souvick Mukherjee, Hector Sandoval, Jessica Aduwo, Tiarnan D. Keenan, Emily Y. Chew, Catherine Cukras:
Semi-supervised learning approach for automatic detection of hyperreflective foci in SD-OCT imaging. - Souvick Mukherjee, Tharindu De Silva, Gopal Jayakar, Peyton Grisso, Henry E. Wiley, Tiarnan D. Keenan, Alisa T. Thavikulwat, Emily Y. Chew, Catherine Cukras:
Retinal layer segmentation for age-related macular degeneration patients with 3D-UNet. - Hristina Uzunova, Leonie Basso, Jan Ehrhardt, Heinz Handels:
Synthesis of annotated pathological retinal OCT data with pathology-induced deformations.
Segmentation
- Shaoyan Pan, Zhen Tian, Yang Lei, Tonghe Wang, Jun Zhou, Mark McDonald, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang:
CVT-Vnet: a convolutional-transformer model for head and neck multi-organ segmentation. - Zhou Zheng, Masahiro Oda, Kazunari Misawa, Kensaku Mori:
Taking full advantage of uncertainty estimation: an uncertainty-assisted two-stage pipeline for multi-organ segmentation. - Abhi Lad, Adithya Narayan, Hari Shankar, Shefali Jain, Pooja Punjani Vyas, Divya Singh, Nivedita Hegde, Jagruthi Atada, Jens Thang, Saw Shier Nee, Arunkumar Govindarajan, Roopa PS, Muralidhar V. Pai, Akhila Vasudeva, Prathima Radhakrishnan, Sripad Krishna Devalla:
Towards a device-independent deep learning approach for the automated segmentation of sonographic fetal brain structures: a multi-center and multi-device validation. - Yichao Li, Mohamed S. Elmahdy, Michael S. Lew, Marius Staring:
Transformation-consistent semi-supervised learning for prostate CT radiotherapy. - Hyeon Dham Yoon, Hyeonjin Kim, Helen Hong:
Deep pancreas segmentation through quantification of pancreatic uncertainty on abdominal CT images.
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