Journal ArticleDOI
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first
TLDR
This commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it.Abstract:
The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has evolved and diversified at a remarkable pace in medical diagnosis, especially in diagnostic imaging. Therefore, this commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it.read more
Citations
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Journal ArticleDOI
Deep learning in medical imaging and radiation therapy.
Berkman Sahiner,Aria Pezeshk,Lubomir M. Hadjiiski,Xiaosong Wang,Karen Drukker,Kenny H. Cha,Ronald M. Summers,Maryellen L. Giger +7 more
TL;DR: The general principles of DL and convolutional neural networks are introduced, five major areas of application of DL in medical imaging and radiation therapy are surveyed, common themes are identified, methods for dataset expansion are discussed, and lessons learned, remaining challenges, and future directions are summarized.
Journal ArticleDOI
Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine
Ryuji Hamamoto,Kruthi Suvarna,Masayoshi Yamada,Kazuma Kobayashi,Norio Shinkai,Mototaka Miyake,Masamichi Takahashi,Shunichi Jinnai,Ryo Shimoyama,Akira Sakai,Ken Takasawa,Amina Bolatkan,Kanto Shozu,Ai Dozen,Hidenori Machino,Satoshi Takahashi,Ken Asada,Masaaki Komatsu,Jun Sese,Syuzo Kaneko +19 more
TL;DR: The history of AI technology as well as the state of the art of medical AI are introduced, focusing on the field of oncology, where AI is expected to play an important role in realizing the current global trend of precision medicine.
Journal ArticleDOI
A framework for breast cancer classification using Multi-DCNNs.
Dina Ahmed Ragab,Dina Ahmed Ragab,Omneya Attallah,Maha Sharkas,Jinchang Ren,Stephen Marshall +5 more
TL;DR: In this article, a new computer-aided diagnosis (CAD) system based on feature extraction and classification using deep learning techniques to help radiologists to classify breast cancer lesions in mammograms is presented.
Journal ArticleDOI
MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks
TL;DR: A novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs that is effective and capable of detecting CO VID-19 and distinguishing it from non-COVID- 19 cases with an accuracy of 94.7%, AUC of 0.98, sensitivity 95, and specificity 93.7%.
Journal ArticleDOI
Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.
TL;DR: An overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for lesion classification and lesion detection for prostate cancer (PCa) diagnosis is provided in this article.
References
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Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Journal ArticleDOI
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
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Radiomics: Images Are More than Pictures, They Are Data.
TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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Deep Learning in Medical Image Analysis
TL;DR: This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.
Journal ArticleDOI
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Babak Ehteshami Bejnordi,Mitko Veta,Paul J. van Diest,Bram van Ginneken,Nico Karssemeijer,Geert Litjens,Jeroen van der Laak,Meyke Hermsen,Quirine F. Manson,Maschenka Balkenhol,Oscar Geessink,N. Stathonikos,Marcory C. R. F. van Dijk,Peter Bult,Francisco Beca,Andrew H. Beck,Dayong Wang,Aditya Khosla,Rishab Gargeya,Humayun Irshad,Aoxiao Zhong,Qi Dou,Qi Dou,Quanzheng Li,Hao Chen,Huangjing Lin,Pheng-Ann Heng,Christian Haß,Elia Bruni,Quincy Wong,Ugur Halici,Mustafa Umit Oner,Rengul Cetin-Atalay,Matt Berseth,Vitali Khvatkov,Alexei Vylegzhanin,Oren Kraus,Muhammad Shaban,Nasir M. Rajpoot,Nasir M. Rajpoot,Ruqayya Awan,Korsuk Sirinukunwattana,Talha Qaiser,Yee-Wah Tsang,David Tellez,Jonas Annuscheit,Peter Hufnagl,Mira Valkonen,Kimmo Kartasalo,Kimmo Kartasalo,Leena Latonen,Pekka Ruusuvuori,Pekka Ruusuvuori,Kaisa Liimatainen,Shadi Albarqouni,Bharti Mungal,Ami George,Stefanie Demirci,Nassir Navab,Seiryo Watanabe,Shigeto Seno,Yoichi Takenaka,Hideo Matsuda,Hady Ahmady Phoulady,Vassili Kovalev,A. Kalinovsky,Vitali Liauchuk,Gloria Bueno,M. Milagro Fernández-Carrobles,Ismael Serrano,Oscar Deniz,Daniel Racoceanu,Daniel Racoceanu,Rui Venâncio +73 more
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.