Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Litjens, Geert, Sánchez, Clara I., Timofeeva, Nadya, Hermsen, Meyke, Nagtegaal, Iris, Kovacs, Iringo, Hulsbergen - van de Kaa, Christina, Bult, Peter +2 more·2016·Scientific Reports

Cited by (1,000)

Items 201–300 of 1,000

  1. Breast Cancer Multi-classification through Deep Neural Network and Hierarchical Classification Approach
    Ghulam Murtaza, Liyana Mohd Shuib, Ghulam Mujtaba, Ghulam Musa Raza · 2019 · Multimedia Tools and Applications
  2. Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
    Ming Y. Lu, Richard J. Chen, Jingwen Wang, Debora Dillon, Faisal Mahmood · 2019 · arXiv (Cornell University)
  3. A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.
    Jiayun Li, Karthik V. Sarma, King Chung Ho, Arkadiusz Gertych, Beatrice S. Knudsen, Corey W Arnold · 2017 · PubMed Central
  4. Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network
    Yiqun Jiang, Jianhao Xiong, H.Y. Li, Xianhong Yang, W.T. Yu, M. Gao, X. Zhao, Yang Ma, W. Zhang, Y.F. Guan et al. · 2019 · British Journal of Dermatology
  5. A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy
    Yoshiki Naito, Masayuki Tsuneki, Noriyoshi Fukushima, Yutaka Koga, Michiyo Higashi, Kenji Notohara, Shinichi Aishima, Nobuyuki Ohike, Takuma Tajiri, Hiroshi Yamaguchi et al. · 2021 · Scientific Reports
  6. Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology—Current Affairs and Perspectives
    Mihaela Moscalu, Roxana Moscalu, Cristina Gena Dascălu, Viorel Ţarcă, Elena Cojocaru, Ioana Mădălina Costin, Elena Țarcă, Ionela Lăcrămioara Șerban · 2023 · Diagnostics
  7. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score
    Óscar Jiménez del Toro, Manfredo Atzori, Sebastian Otálora, Mats Andersson, Kristian Eurén, Martin Hedlund, Peter Rönnquist, Henning Müller · 2017 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
  8. Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization
    Kanghan Oh, Woo‐Sung Kim, Guangfan Shen, Yan Hong Piao, Nam‐In Kang, Il-Seok Oh, Young‐Chul Chung · 2019 · Schizophrenia Research
  9. De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update
    Eugene Lin, Chieh‐Hsin Lin, Hsien‐Yuan Lane · 2022 · Journal of Chemical Information and Modeling
  10. A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images
    Zeyu Gao, Bangyang Hong, Yang Li, Xianli Zhang, Jialun Wu, Chunbao Wang, Xiangrong Zhang, Tieliang Gong, Yefeng Zheng, Deyu Meng et al. · 2022 · Medical Image Analysis
  11. Breast cancer classification using deep learned features boosted with handcrafted features
    Unaiza Sajid, Rizwan Ahmed Khan, Shahid Munir Shah, Sheeraz Arif · 2023 · Biomedical Signal Processing and Control
  12. Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy
    Daisuke Nagasato, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Tomoaki Sonobe, Masahiro Kameoka, Masanori Niki, Ken Hayashi et al. · 2018 · Journal of Ophthalmology
  13. Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images
    Jian Ren, Ilker Hacihaliloglu, Eric A. Singer, David J. Foran, Xin Qi · 2019 · Frontiers in Bioengineering and Biotechnology
  14. ProFlip: Targeted Trojan Attack with Progressive Bit Flips
    Huili Chen, Cheng Fu, Jishen Zhao, Farinaz Koushanfar · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
  15. A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques
    Varsha Nemade, Sunil Kumar Pathak, Ashutosh Kumar Dubey · 2022 · Archives of Computational Methods in Engineering
  16. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
    Kevin Faust, Quin Yuhui Xie, Dominick Han, Kartikay Goyle, Zoya I. Volynskaya, Ugljesa Djuric, Phedias Diamandis · 2018 · BMC Bioinformatics
  17. Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning
    Carl F. Kusche, Tom Reclik, M. Freund, Talal Al‐Samman, U. Kerzel, Sandra Korte‐Kerzel · 2019 · PLoS ONE
  18. Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy
    Toshihiko Nagasawa, Hitoshi Tabuchi, Hiroki Masumoto, Hiroki Enno, Masanori Niki, Zaigen Ohara, Yuki Yoshizumi, Hideharu Ohsugi, Yoshinori Mitamura · 2019 · International Ophthalmology
  19. Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks
    Jingwen Wang, Richard J. Chen, Ming Y. Lu, Alexander Spyridon Baras, Faisal Mahmood · 2020 · IEEE International Symposium on Biomedical Imaging (ISBI)
  20. Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review
    Jahanzaib Latif, Chuangbai Xiao, Shanshan Tu, Sadaqat Ur Rehman, Azhar Imran, Anas Bilal · 2020 · IEEE Access
  21. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer
    Frederik Wessels, Max Schmitt, Eva Krieghoff‐Henning, Tanja B. Jutzi, Thomas Stefan Worst, Frank Waldbillig, Manuel Neuberger, Roman C. Maron, Matthias Steeg, Timo Gaiser et al. · 2021 · British Journal of Urology
  22. Artificial intelligence in healthcare (Review)
    Abdulmohsen Alhejaily · 2024 · Biomedical Reports
  23. Classification of Breast Cancer Histology Images Using Transfer Learning
    Hafiz Mughees Ahmad, Sajid Ghuffar, Khurram Khurshid · 2019 · International Bhurban Conference on Applied Sciences and Technology (IBCAST)
  24. Automated Gleason Scoring and Tumor Quantification in Prostate Core Needle Biopsy Images Using Deep Neural Networks and Its Comparison with Pathologist-Based Assessment
    Han Suk Ryu, Min‐Sun Jin, Jeong Hwan Park, Sang Hun Lee, Joonyoung Cho, Sangjun Oh, Tae-Yeong Kwak, Junwoo Isaac Woo, Yechan Mun, Sun Woo Kim et al. · 2019 · Cancers
  25. Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review
    Ramkumar Thirunavukarasu, C. George Priya Doss, Gnanasambandan Ramanathan, Mohanraj Gopikrishnan, Venketesh Palanisamy · 2022 · Computers in Biology and Medicine
  26. An optimal big data workflow for biomedical image analysis
    Aurelle Tchagna Kouanou, Daniel Tchiotsop, Romanic Kengne, Djoufack Tansaa Zephirin, Ngo Mouelas Ad`ele, Réné Tchinda · 2018 · Informatics in Medicine Unlocked
  27. Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images
    Yajie Hu, Feng Su, Kun Dong, Xinyu Wang, Xinya Zhao, Yumeng Jiang, Jianming Li, Jiafu Ji, Yu Ling Sun · 2021 · Gastric Cancer
  28. Interactive phenotyping of large-scale histology imaging data with HistomicsML
    Michael Nalisnik, Mohamed T. Amgad, Sanghoon Lee, Sameer H. Halani, Jose Enrique Velazquez Vega, Daniel Jay Brat, David A. Gutman, Lee Cooper · 2017 · Scientific Reports
  29. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis
    Sai Kosaraju, Jie Hao, Hyun Min Koh, Mingon Kang · 2020 · Methods
  30. Applications of deep learning in detection of glaucoma: A systematic review
    Delaram Mirzania, Atalie C. Thompson, Kelly W. Muir · 2020 · European Journal of Ophthalmology
  31. Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization
    Bin Li, Adib Keikhosravi, Agnes G. Loeffler, Kevin W. Eliceiri · 2020 · Medical Image Analysis
  32. Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images
    Weiming Mi, Junjie Li, Yucheng Guo, Xinyu Ren, Zhiyong Liang, Tao Zhang, Hao Zou · 2021 · Cancer Management and Research
  33. MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images
    Soham Chattopadhyay, Arijit Dey, Pawan Kumar Singh, Diego A. Oliva, Erik Cuevas, Ram Sarkar · 2022 · Computers in Biology and Medicine
  34. Semantic segmentation for prostate cancer grading by convolutional neural networks
    Zhaoxuan Ma, Jiayun Li, Hootan Salemi, Corey W Arnold, Beatrice S. Knudsen, Arkadiusz Gertych, Nathan Ing · 2018 · SPIE Medical Imaging: Digital and Computational Pathology
  35. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
    Turner, Oliver C., Aeffner, Famke, Bangari, Dinesh S., High, Wanda, Knight, Brian, Forest, Tom, Cossic, Brieuc, Himmel, Lauren E., Rudmann, Daniel G., Bawa, Bhupinder et al. · 2020 · Toxicologic Pathology
  36. Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth
    Yuki Sawada, TAKESHI A. SATO, Masashi Nagaya, Chieko Saito, Hiroyuki Yoshihara, Chihiro Banno, Yosuke Matsumoto, Yukino Matsuda, Kaori Yoshikai, Tomio Sawada et al. · 2021 · Reproductive BioMedicine Online
  37. Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium
    Rachit Kumar Gupta, Mandeep Kaur, Jatinder Manhas · 2019 · Journal of Multimedia Information System
  38. Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification
    Pieter Van Molle, Miguel De Strooper, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt · 2018 · Lecture notes in computer science
  39. Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)
    Neha Baranwal, Preethi Doravari, Renu Kachhoria · 2022 · CRC Press eBooks
  40. Automated assessment of glomerulosclerosis and tubular atrophy using deep learning
    Massimo Salvi, Alessandro Mogetta, Alessandro Gambella, Luca Molinaro, Antonella Barreca, Mauro Giulio Papotti, Filippo Molinari · 2021 · Computerized Medical Imaging and Graphics
  41. Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis
    Nishath Sayed Abdul, Ganiga Channaiah Shivakumar, Sunila Bukanakere Sangappa, Marco Di Blasio, Salvatore Crimi, Marco Cicciù, Giuseppe Minervini · 2024 · BMC Oral Health
  42. Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image
    Yasunari Miyagi, Toshihiro Habara, Rei Hirata, Nobuyoshi Hayashi · 2019 · Reproductive Medicine and Biology
  43. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images
    Yasunari Miyagi, Kazuhiro Takehara, Takahito M. Miyake · 2019 · Molecular and Clinical Oncology
  44. Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks
    Todd Charles Hollon, Balaji Pandian, Esteban Urias, Akshay Save, Arjun Rohit Adapa, Sudharsan Srinivasan, Neil K. Jairath, Zia Farooq, Tamara Marie, Wajd N. Al‐Holou et al. · 2020 · Neuro-Oncology
  45. Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age
    Yasunari Miyagi, Toshihiro Habara, Rei Hirata, Nobuyoshi Hayashi · 2019 · Reproductive Medicine and Biology
  46. Deep Neural Networks Offer Morphologic Classification and Diagnosis of Bacterial Vaginosis
    Zhongxiao Wang, Lei Zhang, Min Zhao, Ying Wang, Huihui Bai, Yufeng Wang, Can Rui, Chong Fan, Jiao Li, Na Li et al. · 2020 · Journal of Clinical Microbiology
  47. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types
    Yasunari Miyagi, Kazuhiro Takehara, Yoko Nagayasu, Takahito M. Miyake · 2019 · Oncology Letters
  48. Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis
    Helena Liz, Manuel A. Sánchez-Montañés, Alfredo Tagarro, Sara Domínguez‐Rodríguez, Ron Dagan, David Camacho · 2021 · Future Generation Computer Systems
  49. C-Net: A reliable convolutional neural network for biomedical image classification
    Hosein Barzekar, Zeyun Yu · 2021 · Expert Systems with Applications
  50. Dimensionality Reduction Strategies for CNN-Based Classification of Histopathological Images
    Silvia Cascianelli, Raquel Bello-Cerezo, Francesco Bianconi, Mario Luca Fravolini, Mehdi Belal, Barbara Palumbo, Jakob Nikolas Kather · 2017 · Smart innovation, systems and technologies
  51. A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images
    Fahdi Kanavati, Gouji Toyokawa, Seiya Momosaki, Hiroaki Takeoka, Masaki Okamoto, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, Masayuki Tsuneki · 2021 · Scientific Reports
  52. Automated Detection and Grading of Non–Muscle-Invasive Urothelial Cell Carcinoma of the Bladder
    Ilaria Jansen, Marit Lucas, Judith Bosschieter, Onno J. de Boer, Sybren Lodewijk Meijer, Ton G. J. M. van Leeuwen, Henk A. Marquering, Jakko A. Nieuwenhuijzen, Daniel Martijn de Bruin, Cemile Dilara Savci-Heijink · 2020 · American Journal Of Pathology
  53. Glomerular disease classification and lesion identification by machine learning
    Chengkun Yang, Ching-Yi Lee, Hsiang‐Sheng Wang, Shun‐Chen Huang, Peir‐In Liang, Jung‐Sheng Chen, Chang‐Fu Kuo, Kun‐Hua Tu, Chao‐Yuan Yeh, Tai‐Di Chen · 2021 · Biomedical Journal
  54. A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection
    Hyunkwang Lee, Mohammad Mansouri, Shahein Tajmir, Michael Howard Lev, Synho Do · 2017 · Journal of Imaging Informatics in Medicine
  55. Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion
    Daisuke Nagasato, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Tomoaki Sonobe, Masahiro Kameoka, Masanori Niki, Yoshinori Mitamura · 2019 · International Journal of Ophthalmology
  56. Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning
    Wen‐Yu Chuang, Shang‐Hung Chang, Wei-Hsiang Yu, Chengkun Yang, Chi‐Ju Yeh, Shir‐Hwa Ueng, Yu-Jen Liu, Tai‐Di Chen, Tai‐Di Chen, Yi-Yin Hsieh et al. · 2020 · Cancers
  57. Untangling the complexity of multimorbidity with machine learning
    Abdelǎali Hassaïne, Gholamreza Salimi‐Khorshidi, Dexter Canoy, Kazem Rahimi · 2020 · Mechanisms of Ageing and Development
  58. DiagSet: a dataset for prostate cancer histopathological image classification
    Michał Koziarski, Bogusław Cyganek, Przemysław Niedziela, Bogusław Olborski, Zbigniew Antosz, Marcin Żydak, Bogdan Kwolek, Paweł Wąsowicz, Andrzej Bukała, Jakub Swadźba et al. · 2024 · Scientific Reports
  59. HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging
    Eduardo Conde‐Sousa, João Vale, Ming Feng, Kele Xu, Yin Wang, Vincenzo Della Mea, David La Barbera, Ehsan Montahaei, Mahdieh Soleymani Baghshah, Andreas Turzynski et al. · 2022 · Journal of Imaging
  60. Comparison of different methods for tissue segmentation in histopathological whole-slide images
    Péter Bándi, Rob van de Loo, Milad Intezar, Daan J. Geijs, Francesco Ciompi, Bram van Ginneken, Jeroen A. W. M. van der Laak, Geert J. S. Litjens · 2017 · IEEE International Symposium on Biomedical Imaging (ISBI)
  61. Artificial intelligence: improving the efficiency of cardiovascular imaging
    Andrew Lin, Márton Kolossváry, Ivana Išgum, Pál Maurovich‐Horvat, Piotr J. Slomka, Damini Dey · 2020 · Expert Review of Medical Devices
  62. A Comprehensive Review on Advancement in Deep Learning Techniques for Automatic Detection of Tuberculosis from Chest X-ray Images
    Evans Kotei, Ramkumar Thirunavukarasu · 2023 · Archives of Computational Methods in Engineering
  63. Detection of whole body bone fractures based on improved YOLOv7
    Junting Zou, Mohd Rizal Arshad · 2024 · Biomedical Signal Processing and Control
  64. A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology
    David R Martin, Joshua Anspach Hanson, Rama R. Gullapalli, Fred A. Schultz, Aisha Sethi, Douglas P. Clark · 2019 · Archives of Pathology & Laboratory Medicine
  65. Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning
    Daisuke Nagasato, Hitoshi Tabuchi, Hiroki Masumoto, Hiroki Enno, Naofumi Ishitobi, Masahiro Kameoka, Masanori Niki, Yoshinori Mitamura · 2019 · PLoS ONE
  66. Role of artificial intelligence in diagnostic oral pathology-A modern approach
    Bhargavi Krishna, Azra Tanveer, PachaVenkat Baghirath, Ashalata Gannepalli · 2020 · Journal of Oral and Maxillofacial Pathology
  67. Artificial intelligence and its potential in oncology
    Londhe, Vaishali Y., Bhasin, Bhavya · 2019 · Drug Discovery Today
  68. Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks
    Fahime Sheikhzadeh, Rabab Kreidieh Ward, Dirk van Niekerk, Martial D. Guillaud · 2018 · PLoS ONE
  69. An Artificial Intelligence–based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies
    Felicia Elena Marginean, Ida Arvidsson, Athanasios Simoulis, Niels Christian Overgaard, Kalle Åström, Anders Heyden, Anders S Bjartell, Agnieszka Krzyzanowska · 2020 · European Urology Focus
  70. Brightness as an Augmentation Technique for Image Classification
    Ibrahem Kandel, Mauro Castelli, Luca Manzoni · 2022 · Emerging Science Journal
  71. Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images
    David Murcia-Gómez, Ignacio Rojas, Olga Valenzuela · 2022 · Applied Sciences
  72. High-throughput ovarian follicle counting by an innovative deep learning approach
    C. Sonigo, S. Jankowski, O. Yoo, O. Trassard, N. Bousquet, M. Grynberg, I. Beau, N. Binart · 2018 · Scientific Reports
  73. Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer
    Yuan Gao, Zhengdong Zhang, Shuo Li, Yuting Guo, Qingyao Wu, Shuhao Liu, Shujian Yang, Lei Ding, Baochun Zhao, Shuai Li et al. · 2019 · Chinese Medical Journal
  74. Metabolic Imaging Based Sub-Classification of Lung Cancer
    Mustafa Bicakci, Oğuzhan Ayyıldız, Zafer Aydın, Alper Baştürk, Seyhan Karaçavuş, Bülent Yılmaz · 2020 · IEEE Access
  75. Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens
    Jon N. Marsh, Ta‐Chiang Liu, Parker C. Wilson, S. Joshua Swamidass, Joseph P. Gaut · 2021 · JAMA Network Open
  76. Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features
    Ibrahim Onur Sığırcı, Abdülkadir Albayrak, Gökhan Bilgin · 2021 · Multimedia Tools and Applications
  77. Artificial intelligence in breast cancer histopathology
    Ronald CK Chan, Chun Kit Curtis To, Ka Chuen Tom Cheng, Tada Yoshikazu, Lai Ling Amy Yan, Gary Man-Kit Tse · 2022 · Histopathology
  78. A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network
    Muhammet Fatih Aslan · 2022 · Computers & Electrical Engineering
  79. Nuclear Architecture Analysis of Prostate Cancer via Convolutional Neural Networks
    Jin Tae Kwak, Stephen M. Hewitt · 2017 · IEEE Access
  80. Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT
    Tomoaki Sonobe, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Masumoto, Naohumi Ishitobi, Shoji Morita, Hiroki Enno, Daisuke Nagasato · 2018 · International Ophthalmology
  81. Multiview boosting digital pathology analysis of prostate cancer
    Jin Tae Kwak, Stephen M. Hewitt · 2017 · Computer Methods and Programs in Biomedicine
  82. Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level
    Lucy G. Hutchinson, Bernhard Steiert, Antoine Soubret, Jonathan Wagg, Alex Phipps, Richard Peck, Jean‐Eric Charoin, Benjamin Ribba · 2018 · CPT Pharmacometrics & Systems Pharmacology
  83. Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach
    Jie Dong, Lei Li, Shengxiang Liang, Shujun Zhao, Bin Zhang, Yun Meng, Yong Zhang, Suxiao Li · 2020 · Academic Radiology
  84. Deep learning for image analysis: Personalizing medicine closer to the point of care
    Quin Yuhui Xie, Kevin Faust, Randy Van Ommeren, Adeel Sheikh, Ugljesa Djuric, Phedias Diamandis · 2019 · Critical Reviews in Clinical Laboratory Sciences
  85. Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease
    Konstantinos Exarchos, Agapi Aggelopoulou, Aikaterini Oikonomou, Theodora Biniskou, Vasiliki Beli, Eirini Antoniadou, Konstantinos Kostikas · 2021 · IEEE Journal of Biomedical and Health Informatics
  86. Deep learning based Nucleus Classification in pancreas histological images
    Young Hwan Chang, Guillaume Thibault, Owen Madin, Vahid Azimi, Cole Meyers, Brett Johnson, Jason M. Link, Adam Margolin, Joe W. Gray · 2017 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  87. Breast Cancer Histology Image Classification Based on Deep Neural Networks
    Yao Guo, Huihui Dong, Fangzhou Song, Chuang Zhu, Jun Liu · 2018 · Lecture notes in computer science
  88. Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies—Current Status and Next Steps
    Kimmo Kartasalo, Wouter Bulten, Brett Delahunt, Po-Hsuan Cameron Chen, Hans Pinckaers, Henrik Olsson, Xiaoyi Ji, Nita Mulliqi, Hemamali Samaratunga, Toyonori Tsuzuki et al. · 2021 · European Urology Focus
  89. An autonomous channel deep learning framework for blood glucose prediction
    Tao Yang, Xia Yu, Ning Ma, Ruikun Wu, Hongru Li · 2022 · Applied Soft Computing
  90. Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
    Stephanie Noronha, Mayuri A. Mehta, Dweepna Garg, Ketan V. Kotecha, Ajith Abraham · 2023 · IEEE Access
  91. Correntropy-based robust multilayer extreme learning machines
    Liangjun Chen, Paul Honeiné, Hua Qu, Jihong Zhao, Xia Sun · 2018 · Pattern Recognition
  92. Carcinoma Type Classification From High-Resolution Breast Microscopy Images Using a Hybrid Ensemble of Deep Convolutional Features and Gradient Boosting Trees Classifiers
    Ritabrata Sanyal, Devroop Kar, Ram Sarkar · 2021 · IEEE Transactions on Computational Biology and Bioinformatics
  93. Cancer Surgery 2.0: Guidance by Real-Time Molecular Technologies
    Nina Ogrinc, Philippe Saudemont, Zoltán Takáts, Michel Salzet, Isabelle Fournier · 2021 · Trends in Molecular Medicine
  94. Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk
    Suzanne C. Wetstein, Allison M. Onken, Christina I. Luffman, Gabrielle M. Baker, Michael E. Pyle, Kevin H. Kensler, Ying Liu, Bart Bakker, Ruud Vlutters, Marinus B. van Leeuwen et al. · 2020 · PLoS ONE
  95. Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes
    Irbaz Bin Riaz, Stephanie A. Harmon, Zhijun Chen, Syed Arsalan Ahmed Naqvi, Liang Cheng C · 2024 · American Society of Clinical Oncology Educational Book
  96. A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia
    Adam Shephard, Raja Muhammad Saad Bashir, Hanya Mahmood, Mostafa Jahanifar, Fayyaz Minhas, Shan E Ahmed Raza, Kris D. McCombe, Stephanie G. Craig, Jacqueline A. James, Jill M. Brooks et al. · 2024 · npj Precision Oncology
  97. Deep Learning Models Combining for Breast Cancer Histopathology Image Classification
    Hela Elmannai, Monia Hamdi, Abeer D. Algarni · 2021 · International Journal of Computational Intelligence Systems
  98. Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain
    Anuj Kumar, Ashish Kumar Jha, Jai Prakash Agarwal, Manender Yadav, Suvarna Badhe, Ayushi Sahay, Sridhar Epari, Arpita Sahu, Kajari Bhattacharya, Abhishek Chatterjee et al. · 2023 · Journal of Personalized Medicine
  99. Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities
    Wahiba Oualikene-Gonin, Marie‐Christine Jaulent, Jean-Pierre Thierry, Sofia Oliveira‐Martins, Laëtitia Belgodère, Patrick Maison, Joël Ankri, The Scientific Advisory Board of ANSM · 2024 · Frontiers in Pharmacology
  100. Detection of glomeruli in renal pathology by mutual comparison of multiple staining modalities
    Maja Temerinac-Ott, Germain Forestier, Jessica Schmitz, Meyke Hermsen, Jan Hinrich Bräsen, Friedrich Feuerhake, Cédric Wemmert · 2017 · International symposium on image and signal processing and analysis/ISPA ...