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AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics [article]

Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
2022 arXiv   pre-print
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks.  ...  There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features.  ...  Acknowledgements This project was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, and the Canadian Institutes of Health Research  ... 
arXiv:2110.10332v4 fatcat:vmpxhoolarbrve5ddyfn5umfim

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

Iterative augmentation of visual evidence for weakly-supervised lesion localization in deep interpretability frameworks: application to color fundus images

Cristina Gonzalez-Gonzalo, Bart Liefers, Bram van Ginneken, Clara I. Sanchez
2020 IEEE Transactions on Medical Imaging  
of weakly-supervised localization of different types of DR and AMD abnormalities, both qualitatively and quantitatively.  ...  and network architectures.  ...  Iterative Visual Evidence Augmentation Let I ∈ R m×n×3 be an image with size m × n pixels (and 3 color channels) and a corresponding label y, F cnn : I −→ŷ ∈ R a convolutional neural network (CNN) optimized  ... 
doi:10.1109/tmi.2020.2994463 pmid:32746093 fatcat:enrasntk5nagffq2hu5ceqbo2q

Iterative augmentation of visual evidence for weakly-supervised lesion localization in deep interpretability frameworks [article]

Cristina González-Gonzalo, Bart Liefers, Bram van Ginneken, Clara I. Sánchez
2019 arXiv   pre-print
We evaluate the generated visual evidence and the performance of weakly-supervised localization of different types of DR and AMD abnormalities, both qualitatively and quantitatively.  ...  This yields augmented visual evidence, including less discriminative lesions which were not detected at first but should be considered for final diagnosis.  ...  Iterative visual evidence augmentation Let I ∈ R m×n×3 be an image with size m × n pixels (and 3 color channels) and a corresponding label y, F cnn : I −→ŷ ∈ R a convolutional neural network (CNN) optimized  ... 
arXiv:1910.07373v1 fatcat:lq2qnewd45gkjfehopp4dokog4

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Decisions Aggregated CNN Mohammad Arafat Hussain*; Ghassan Hamarneh; Rafeef Abugharbieh T-40 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase  ...  Agar Plates by Convolutional Neural Networks Mattia Savardi; Sergio Benini; Alberto Signoroni* M-111 A Pixel-wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection Maria  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Deep Learning in Medical Ultrasound Image Segmentation: a Review [article]

Ziyang Wang
2021 arXiv   pre-print
It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization.  ...  In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first.  ...  Compared with the supervised training for CNN-based network, some researchers study on weakly supervised learning, because weakly labeled data requires low cost in labeling.  ... 
arXiv:2002.07703v3 fatcat:dosuiqzoh5e6tm4754wmxeifam

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies.  ...  Despite its safety and efficacy, QUS suffers from several major drawbacks: poor imaging quality, inter- and intra-observer variability which hampers the reproducibility of measurements.  ...  RNN with long short-term memory (LSTM) cells was used for prostate cancer classification while residual neural networks and dilated CNN were used for prostate segmentation.  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

Advances in Deep Learning-Based Medical Image Analysis

Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu
2021 Health Data Science  
With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active  ...  This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications.  ...  Convolutional neural network (CNN) is the dominant classification framework for image analysis [24] . With the development of deep learning, the framework of CNN has continuously improved.  ... 
doi:10.34133/2021/8786793 fatcat:d6nkb4yoxrcgni4y5owju5pnh4

A Survey on Graph-Based Deep Learning for Computational Histopathology [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology  ...  and classification, tumor invasion and staging, image retrieval, and survival prediction.  ...  The concept of constructing a graph and then using geodesic distance for community detection has outperformed deep neural networks and graph-based deep leaning methods such as ChebNet, GCNs and deep graph  ... 
arXiv:2107.00272v2 fatcat:3eskkeref5ccniqsjgo3hqv2sa

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.  ...  Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded.  ... 
doi:10.1016/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Deep Neural Networks for Medical Image Segmentation

Priyanka Malhotra, Sheifali Gupta, Deepika Koundal, Atef Zaguia, Wegayehu Enbeyle, Chinmay Chakraborty
2022 Journal of Healthcare Engineering  
This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks.  ...  The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks.  ...  in[73]extended faster R-CNN to present Mask R-CNN for instance segmentation.emodelcan detect objects in a given image and generates a high-quality segmentation mask for each object in an image.It uses  ... 
doi:10.1155/2022/9580991 pmid:35310182 pmcid:PMC8930223 fatcat:oylwslatk5bcpocg45ro32shbq

A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises [article]

S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
2020 arXiv   pre-print
It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing.  ...  We conclude with a discussion and presentation of promising future directions.  ...  Weakly or partially supervised learning. In [54] , Wang et al. solve a weakly-supervised multi-label disease classification from a chest x-ray.  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis [article]

Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang, Xiaoqiong Li
2019 arXiv   pre-print
The success of deep learning has been witnessed as a promising technique for computer-aided biomedical image analysis, due to end-to-end learning framework and availability of large-scale labelled samples  ...  To be more practical for biomedical image analysis, in this paper we survey the key SSL techniques that help relieve the suffering of deep learning by combining with the development of related techniques  ...  Acknowledgements The authors would like to thank members of the Medical Image Analysis for discussions and suggestions.  ... 
arXiv:1908.00473v1 fatcat:atotvdxp6janve2mz3swyf47xa

Front Matter: Volume 11314

Horst K. Hahn, Maciej A. Mazurowski
2020 Medical Imaging 2020: Computer-Aided Diagnosis  
of glioma 11314 2U Neural networks for in situ detection of glioma infiltration using optical coherence tomography 11314 2V A data-driven approach for stratifying psychotic and mood disorders subjects  ...  similarity index 11314 04 Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution  ...  He ensured that the BRH, and subsequently the CDRH, was a sponsor for the early and subsequent Medical Imaging meetings, helping to launch and ensure the historical success of the meeting.  ... 
doi:10.1117/12.2570720 fatcat:vnrxbpseqnfpnjnxyjzf6boiwe

Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study

Veronika Shavlokhova, Sameena Sandhu, Christa Flechtenmacher, Istvan Koveshazi, Florian Neumeier, Víctor Padrón-Laso, Žan Jonke, Babak Saravi, Michael Vollmer, Andreas Vollmer, Jürgen Hoffmann, Michael Engel (+2 others)
2021 Journal of Clinical Medicine  
In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time.  ...  A CNN architecture (MobileNet) was trained and tested for accuracy.  ...  The authors I.K. and F.N. are working for M3i GmbH, Schillerstr. 53, 80336 Munich, Germany and provided support in building the machine learning model.  ... 
doi:10.3390/jcm10225326 pmid:34830608 pmcid:PMC8618824 fatcat:ckjtup5dhrcelowtqwrydfv2py
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