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Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results

Vishwa S. Parekh, Katarzyna J. Macura, Susan C. Harvey, Ihab R. Kamel, Riham EI‐Khouli, David A. Bluemke, Michael A. Jacobs
2019 Medical Physics (Lancaster)  
The breast tissue signatures are used as inputs in a stacked sparse autoencoder (SSAE) multiparametric deep learning (MPDL) network for segmentation of breast mpMRI.  ...  Deep learning is emerging in radiology due to the increased computational capabilities available to reading rooms.  ...  39 Amit et al. 38 developed a hybrid mass-detection algorithm that combines unsupervised candidate detection with deep learning-based classification and saliency maps.  ... 
doi:10.1002/mp.13849 pmid:31598978 pmcid:PMC7003775 fatcat:mkzx53uognfwxd3fu3vpf5g2aa

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  
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year.  ...  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.  ...  Appendix A: Literature selection Pubmed was searched for papers containing "convolutional" OR "deep learning" in any field.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Towards Trainable Saliency Maps in Medical Imaging [article]

Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, Praveer Singh
2020 arXiv   pre-print
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely  ...  While saliency methods attempt to tackle this problem in non-medical contexts, their apriori explanations do not transfer well to medical usecases.  ...  Hybrid mass detec- tion in breast mri combining unsupervised saliency analysis and deep learning.  ... 
arXiv:2011.07482v1 fatcat:n3uzr6wifngqdhubypjusywpp4

Computational biology: deep learning

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
2017 Emerging Topics in Life Sciences  
Deep learning is the trendiest tool in a computational biologist's toolbox.  ...  In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics.  ...  age was shown to be heritable Age prediction Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography [107] CNN Image (mammography  ... 
doi:10.1042/etls20160025 pmid:33525807 pmcid:PMC7289034 fatcat:qnw2yndsp5aqlnxxshtaipzctu

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision.  ...  Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease.  ...  In medical image analysis, unsupervised learning algorithms have also been studied; These include Deep Belief Networks (DBNs), Restricted Boltzmann Machines (RBMs), Autoencoders, and Generative Adversarial  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

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  
X-ray Breast Mass Segmentation and Shape Classification Vivek Kumar Singh*; Santiago Romani; Hatem A.  ...  and pN-stage Classification In Breast Cancer Byungjae Lee*; Kyunghyun Paeng T-60 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes Siqi  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

An overview of deep learning in medical imaging [article]

Imran Ul Haq
2022 arXiv   pre-print
difficulties, lessons learned and future of DL in the field of medical science.  ...  This model was a kind of convolutional neural system (CNN) called deep learning (DL). Since then, researchers have started to participate efficiently in DL's fastest developing area of research.  ...  Using transfer learning, a deep CNN was trained on general tasks to classify breast cancer.  ... 
arXiv:2202.08546v1 fatcat:tg32btcm5vdsnlzeuhdttozj6m

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

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS.  ...  Finally, challenges and future potential AI applications in QUS are discussed.  ...  A binary cross-entropy loss and a batch-based Dice loss were combined into the stage-wise hybrid loss function for deep supervision learning.  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.  ...  In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection  ...  Multi-task learning and multi-modal learning In [203] , the multi-task learning is adopted, where the data of brain MRI, breast MRI and cardiac CT angiography (CTA) are used simultaneously as multiple  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images.  ...  classi- fier Private set -381 images Xu et al. (2019) Breast H&E Detection of breast cancer Deep hybrid attention (CNN + LSTM) network BreakHis (7,909 images) Zhang et al. (2019) ( ) Bladder  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Noor Salah Hassan, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dathar A. Hasan
2021 Asian Journal of Research in Computer Science  
This review presents a comparison has been done in term of accuracy among many techniques used for detecting breast cancer in medical images.  ...  It is also assisting doctors in their daily work by creating algorithms and software to analyze the medical images that can identify early signs of breast cancer.  ...  Expert radiologists are needed to predict breast mass and type of mass.  ... 
doi:10.9734/ajrcos/2021/v9i130212 fatcat:rpojdlwivnhztb2tghcwiq4ipi

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Eyad Elyan, Pattaramon Vuttipittayamongkol, Pamela Johnston, Kyle Martin, Kyle McPherson, Carlos Francisco Moreno-García, Chrisina Jayne, Md. Mostafa Kamal Sarker
2022 Artificial Intelligence Surgery  
The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding  ...  We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices  ...  [119] MRI Breast tumors classification BI-RADS Pre-trained CNNs 2 [96] Ultrasound Breast tumor segmentation BUS CIA cGAN 2 [120] DBT and X-ray Breast mass segmentation DBT U-Net 2 [121] Cardiac  ... 
doi:10.20517/ais.2021.15 fatcat:o5o4uk5mivcyfpmqbxbfua2tu4

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Masaaki Komatsu, Akira Sakai, Ai Dozen, Kanto Shozu, Suguru Yasutomi, Hidenori Machino, Ken Asada, Syuzo Kaneko, Ryuji Hamamoto
2021 Biomedicines  
However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities.  ...  Artificial intelligence (AI) is being increasingly adopted in medical research and applications.  ...  Acknowledgments: We would like to thank all members of the Hamamoto Laboratory, who provided valuable advice and a comfortable research environment.  ... 
doi:10.3390/biomedicines9070720 fatcat:aj5jsjjglbhfnhhzslnkp5zahy

Shape Detection In 2D Ultrasound Images [article]

Ruturaj Gole, Haixia Wu, Subho Ghose
2019 arXiv   pre-print
Hybrid neural networks, linear and logistic regression models, 3D reconstructed models, and various machine learning techniques have been used to solve complex problems such as detection of lesions and  ...  The dependence on subjective opinions of experts such as radiologists calls for an automatic recognition and detection system that can provide an objective analysis.  ...  Multiple deep learning techniques are compared with each other and are even combined in some papers.  ... 
arXiv:1911.09863v1 fatcat:ek56tdhcavdcbo3ok5sjjofq4q

Machine Learning Methods for Histopathological Image Analysis: A Review [article]

Jonathan de Matos and Steve Tsham Mpinda Ataky and Alceu de Souza Britto Jr. and Luiz Eduardo Soares de Oliveira and Alessandro Lameiras Koerich
2021 arXiv   pre-print
In this paper, we present a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods.  ...  We also cover the most common tasks in HI analysis, such as segmentation and feature extraction.  ...  Finally, the category of deep methods contains works focused on supervised and unsupervised learning of different architectures of deep neural networks.  ... 
arXiv:2102.03889v1 fatcat:ylrsildl4nenho22erndvpjcjy
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