Most cited papers in Digital Imaging for Blood Diseases
What are the most cited papers in Digital Imaging for Blood Diseases?
The most cited paper in Digital Imaging for Blood Diseases is "Artificial neural networks: a tutorial" (Abhishek Jain, Jianchang Mao, Khalid Mohiuddin, 1996), with 4,984 citations. Next come "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning" (4,903) and "Ridge-Based Vessel Segmentation in Color Images of the Retina" (4,213).
Showing 50 of 100 papers. Every paper with more than 476 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Artificial neural networks: a tutorial Abhishek Jain, Jianchang Mao, Khalid Mohiuddin · Computer | Mar 1996 | 4,984 |
| 2 | Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning Daniel Kermany et al. · Cell | Feb 2018 | 4,903 |
| 3 | Ridge-Based Vessel Segmentation in Color Images of the Retina Joes Staal, Michael D. Abràmoff, Meindert Niemeijer et al. · IEEE Transactions on Medical Imaging | Apr 2004 | 4,213 |
| 4 | Clinical-grade computational pathology using weakly supervised deep learning on whole slide images Gabriele S. V. Campanella, Matthew G. Hanna, Luke Geneslaw et al. · Nature Medicine | 15 Jul 2019 | 2,845 |
| 5 | The Immunoglobulin Superfamily—Domains for Cell Surface Recognition Abigail Williams, A. Neil Barclay · Annual Review of Immunology | Apr 1988 | 2,259 |
| 6 | Histopathological Image Analysis: A Review Metin N. Gürcan, Laura E. Boucheron, Ali Can et al. · IEEE Reviews in Biomedical Engineering | 2009 | 2,075 |
| 7 | U-Net: deep learning for cell counting, detection, and morphometry Thorsten Falk et al. · Nature Methods | 4 Dec 2018 | 2,057 |
| 8 | A Dataset for Breast Cancer Histopathological Image Classification Fábio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean et al. · IEEE Transactions on Biomedical Engineering | 30 Oct 2015 | 1,840 |
| 9 | Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification João V. B. Soares, J. J. G. Leandro, Roberto Marcondes Cesar Junior et al. · IEEE Transactions on Medical Imaging | 23 Aug 2006 | 1,508 |
| 10 | Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases Andrew R. Janowczyk, Anant Madabhushi · Journal of Pathology Informatics | 2016 | 1,377 |
| 11 | Learning methods for generic object recognition with invariance to pose and lighting Yann LeCun, Fu Jie Huang, Léon Bottou | 12 Nov 2004 | 1,352 |
| 12 | Brain tumor classification using deep CNN features via transfer learning S. Deepak, P. M. Ameer · Computers in Biology and Medicine | 29 Jun 2019 | 1,323 |
| 13 | Digital pathology and artificial intelligence Muhammad Khalid Khan Niazi, Anil Vasdev Parwani, Metin N. Gürcan · The Lancet Oncology | 30 Apr 2019 | 1,251 |
| 14 | Ilastik: Interactive learning and segmentation toolkit Christoph Markus Sommer, Christoph Straehle, Ullrich Köthe et al. | Mar 2011 | 1,230 |
| 15 | On calibration of modern neural networks Chuan Jun Guo, Geoff Pleiss, Yu Sun et al. · International Conference on Machine Learning | 17 Jul 2017 | 1,168 |
| 16 | Image analysis and machine learning in digital pathology: Challenges and opportunities Anant Madabhushi, George Lee · Medical Image Analysis | 4 Jul 2016 | 1,125 |
| 17 | Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm Nameirakpam Dhanachandra, Khumanthem Manglem, Yambem Jina Chanu · Procedia Computer Science | 2015 | 1,117 |
| 18 | Deep convolutional neural network based medical image classification for disease diagnosis Samir S. Yadav, Shivajirao Manikrao Jadhav · Journal Of Big Data | Dec 2019 | 1,089 |
| 19 | Classification using deep learning neural networks for brain tumors Heba Mohsen, El‐Sayed A. El‐Dahshan, El-Sayed M. El-Horbaty et al. · Future Computing and Informatics Journal | 23 Dec 2017 | 1,082 |
| 20 | A New Blood Diluent for Counting the Erythrocytes and Leucocytes of the Chicken Michael P. Natt, Chester A. Herrick · Poultry Science | Jul 1952 | 1,061 |
| 21 | An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe et al. · IEEE Transactions on Biomedical Engineering | 22 Jun 2012 | 1,036 |
| 22 | Classification of breast cancer histology images using Convolutional Neural Networks Teresa Araújo, Guilherme Aresta, Eduardo Castro et al. · PLoS ONE | Jun 2017 | 1,030 |
| 23 | Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net) Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha et al. | Jul 2018 | 1,020 |
| 24 | Blood vessel segmentation methodologies in retinal images – A survey Muhammad Moazam Fraz, Paolo Remagnino, Andreas Hoppe et al. · Computer Methods and Programs in Biomedicine | 21 Apr 2012 | 1,007 |
| 25 | Convolutional Neural Networks for Diabetic Retinopathy Harry Pratt, Frans Coenen, Deborah Broadbent et al. · Procedia Computer Science | 2016 | 997 |
| 26 | Weighted Res-UNet for High-Quality Retina Vessel Segmentation Xiao Xiao, Sheng Lian, Zhiming Luo et al. | Oct 2018 | 976 |
| 27 | COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images Ezz El‐Din Hemdan, Marwa Ahmed Shouman, Mohamed Esmail Karar · arXiv (Cornell University) | 24 Mar 2020 | 973 |
| 28 | Breast cancer histopathological image classification using Convolutional Neural Networks Fábio Alexandre Spanhol, Luiz S. Oliveira, Caroline Petitjean et al. | Jul 2016 | 957 |
| 29 | Machine Learning Methods for Histopathological Image Analysis Daisuke Komura, Shumpei Ishikawa · Computational and Structural Biotechnology Journal | 2018 | 955 |
| 30 | A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features D. Marín, Arturo Aquino, M. E. Arias et al. · IEEE Transactions on Medical Imaging | 10 Aug 2010 | 939 |
| 31 | Segmenting Retinal Blood Vessels With Deep Neural Networks Paweł Liskowski, Krzysztof Krawiec · IEEE Transactions on Medical Imaging | 24 Mar 2016 | 914 |
| 32 | A visual-language foundation model for computational pathology Ming Y. Lu, Bowen Chen, Drew F. K. Williamson et al. · Nature Medicine | Mar 2024 | 896 |
| 33 | DUNet: A deformable network for retinal vessel segmentation Qiangguo Jin, Zhaopeng Meng, Tuan D. Pham et al. · Knowledge-Based Systems | 3 May 2019 | 885 |
| 34 | Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images Jun Xu, Lei Xiang, Qingshan Liu et al. · IEEE Transactions on Medical Imaging | 20 Jul 2015 | 872 |
| 35 | Deep Learning to Improve Breast Cancer Detection on Screening Mammography Li Shen, Laurie R. Margolies, Joseph H. Rothstein et al. · Scientific Reports | 29 Aug 2019 | 859 |
| 36 | Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images Abhishek Vahadane, Tingying Peng, Amit Sethi et al. · IEEE Transactions on Medical Imaging | 28 Apr 2016 | 848 |
| 37 | A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina Thomas Walter, J.C. Klein, Pascale Massin et al. · IEEE Transactions on Medical Imaging | Oct 2002 | 827 |
| 38 | Semi-Supervised Learning on Riemannian Manifolds Mikhail Belkin, Partha Niyogi · Machine Learning | 25 Jun 2004 | 812 |
| 39 | ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training Hugo Touvron, Piotr Bojanowski, Mathilde Caron et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 12 Sep 2022 | 807 |
| 40 | Neuronal morphometry directly from bitmap images Tiago Ferreira, Arne V. Blackman, Julia Oyrer et al. · Nature Methods | 29 Sep 2014 | 799 |
| 41 | The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis Marianne E Sinka, Michael John Bangs, Sylvie Manguin et al. · Parasites & Vectors | Dec 2010 | 798 |
| 42 | Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks Todd Charles Hollon et al. · Nature Medicine | 2020 | 797 |
| 43 | Comparative study of retinal vessel segmentation methods on a new publicly available database Meindert Niemeijer, Joes Staal, Bram van Ginneken et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE | 12 May 2004 | 797 |
| 44 | Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels Adam W. Hoover, Michael H. Goldbaum · IEEE Transactions on Medical Imaging | 29 Jul 2003 | 796 |
| 45 | A novel deep learning based framework for the detection and classification of breast cancer using transfer learning SanaUllah Khan, Naveed Islam, Zahoor Jan et al. · Pattern Recognition Letters | 28 Mar 2019 | 794 |
| 46 | Artificial intelligence in retina Ursula Margarethe Schmidt-Erfurth, Amir Sadeghipour, Bianca S. Gerendas et al. · Progress in Retinal and Eye Research | Aug 2018 | 788 |
| 47 | Deep neural network models for computational histopathology: A survey Chetan L. Srinidhi, Ozan Ciga, Anne Louise Martel · Medical Image Analysis | 25 Sep 2020 | 764 |
| 48 | Deep learning approaches for COVID-19 detection based on chest X-ray images Aras Masood Ismael, Abdulkadir Şengür · Expert Systems with Applications | 28 Sep 2020 | 759 |
| 49 | Detection of Coronavirus Disease (COVID-19) Based on Deep Features Prabira Kumar Sethy, Santi Kumari Behera · Preprints.org | 19 Mar 2020 | 755 |
| 50 | Joint Optimization Framework for Learning with Noisy Labels Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki et al. | Jun 2018 | 732 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.