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SPIE Medical Imaging: Computer-Aided Diagnosis

Source type: conference
Alternate names: Medical Imaging 2022: Computer-Aided Diagnosis, Medical Imaging 2007: Computer-Aided Diagnosis, Medical Imaging 2018: Computer-Aided Diagnosis, Medical Imaging 2019: Computer-Aided Diagnosis, Medical Imaging 2020: Computer-Aided Diagnosis
Fully OA: No
DOAJ: No
OpenAlex ID: S4363606689
Polyp-SAM: transfer SAM for polyp segmentation
2024 · Yuheng Li, Mingzhe Hu, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 133
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Deep 3D convolution neural network for CT brain hemorrhage classification
2018 · Kamal Jnawali, Mohammad R. Arbabshirani, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 123
Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans
2018 · Sindhu Ramachandran, Jose George, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 120
Anomaly detection for medical images based on a one-class classification
2018 · Qi Wei, Bibo Shi, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 84
Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI
2020 · Zachary Papanastasopoulos, Ravi K. Samala, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 78
MRI image harmonization using cycle-consistent generative adversarial network
2020 · Gourav Modanwal, Adithya Vellal, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 67
A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI
2018 · Kavya Ravichandran, Nathaniel Braman, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 63
A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes
2018 · Daisuke Sato, Shouhei Hanaoka, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 62
Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs
2019 · Sivaramakrishnan Rajaraman, George Thoma, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 55
Classification of brain MRI with big data and deep 3D convolutional neural networks
2018 · Viktor Wegmayr, Sai Aitharaju, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 55
Comparing deep learning models for population screening using chest radiography
2018 · R. Sivaramakrishnan, Sameer Antani, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 54
Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features
2022 · Mohammadreza Salmanpour, Mahdi Hosseinzadeh, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 49
Early detection of retinopathy of prematurity stage using deep learning approach
2019 · Supriti Mulay, Keerthi Ram, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 49
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CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation
2019 · Youbao Tang, Sooyoun Oh, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 46
Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images
2018 · Xiangrong Zhou, Kuzuma Yamada, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 44
General purpose radiomics for multi-modal clinical research
2019 · Michael G. Wels, Félix Lades, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 43
Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks
2020 · Ravi K. Samala, Heang-Ping Chan, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 42
Automatic multi-organ segmentation in thorax CT images using U-Net-GAN
2019 · Xiaofeng Yang, Yang Lei, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 42
Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears
2020 · Feng Yang, Nicolas Quizon, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 36
Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images
2018 · Hirohisa Oda, Kanwal K. Bhatia, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 35
A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography
2019 · Lambros S. Athanasiou, Max L. Olender, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 34
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Multi-resolution CNN for brain vessel segmentation from cerebrovascular images of intracranial aneurysm: a comparison of U-Net and DeepMedic
2020 · Tatsat Rajendra Patel, Nikhil Paliwal, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 32
Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography
2020 · Tomoki Uemura, Janne J. Näppi, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 32
Age prediction using a large chest x-ray dataset
2019 · Alexandros Karargyris, Satyananda Kashyap, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 32
Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis
2019 · Nina Tubau, Priscille de Dumast, et al. · SPIE Medical Imaging: Computer-Aided Diagnosis · 32
Works count: 1,175
Citation count: 5,345
H-index: 29
I10-index: 134
2yr mean citedness: 1.199
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