Michael Kawczynski

1.1k total citations · 1 hit paper
10 papers, 486 citations indexed

About

Michael Kawczynski is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michael Kawczynski has authored 10 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Ophthalmology and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michael Kawczynski's work include Retinal Diseases and Treatments (6 papers), Retinal Imaging and Analysis (5 papers) and Retinal and Optic Conditions (3 papers). Michael Kawczynski is often cited by papers focused on Retinal Diseases and Treatments (6 papers), Retinal Imaging and Analysis (5 papers) and Retinal and Optic Conditions (3 papers). Michael Kawczynski collaborates with scholars based in United States, Switzerland and Germany. Michael Kawczynski's co-authors include Youngho Seo, Jae Ho Sohn, Roy Harnish, Lorenzo Nardo, Carina Marí Aparici, Dmytro Lituiev, Nathaniel W. Jenkins, Miguel Hernandez Pampaloni, Mariam Aboian and Spencer C. Behr and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiology and Investigative Ophthalmology & Visual Science.

In The Last Decade

Michael Kawczynski

10 papers receiving 475 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using ¹⁸F-FDG PET of the Brain

2018 379 citations Yiming Ding, Jae Ho Sohn et al. Radiology profile →

Peers

Countries citing papers authored by Michael Kawczynski

Since Specialization

Citations

This map shows the geographic impact of Michael Kawczynski's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Kawczynski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Kawczynski more than expected).

Fields of papers citing papers by Michael Kawczynski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Kawczynski. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Kawczynski. The network helps show where Michael Kawczynski may publish in the future.

Co-authorship network of co-authors of Michael Kawczynski

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Kawczynski. A scholar is included among the top collaborators of Michael Kawczynski based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Kawczynski. Michael Kawczynski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

#	Work	Indexed citations
1	Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration 2023 ·SHILAP Revista de lepidopterología ·Andreas Maunz, (unknown), Michael Kawczynski, Jian S. Dai, Aaron Lee, Richard F. Spaide, Jayashree Sahni, Daniela Ferrara	10
2	Geographic Atrophy Segmentation Using Multimodal Deep Learning 2023 ·Translational Vision Science & Technology ·Theodore Spaide, (unknown), (unknown), Neha Anegondi, (unknown), Michael Kawczynski, Elizabeth M. Newton, (unknown), Simon S. Gao, Aaron Lee, Frank G. Holz, Srinivas R. Sadda, Steffen Schmitz-Valckenberg, Daniela Ferrara	21
3	Machine Learning to Predict Faricimab Treatment Outcome in Neovascular Age-Related Macular Degeneration 2023 ·SHILAP Revista de lepidopterología ·(unknown), Michael Kawczynski, Neha Anegondi, Aleš Neubert, (unknown), Daniela Ferrara, Carlos Quezada-Ruiz	4
4	Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging 2022 ·Ophthalmology Retina ·Neha Anegondi, Simon S. Gao, (unknown), Richard F. Spaide, Srinivas R. Sadda, Frank G. Holz, (unknown), Lee Honigberg, Elizabeth M. Newton, (unknown), Michael Kawczynski, Thomas Bengtsson, Daniela Ferrara, Qi Yang	33
5	Analysis of numerical feature extraction from automated geographic atrophy segmentation 2021 ·Investigative Ophthalmology & Visual Science ·Theodore Spaide, (unknown), (unknown), Neha Anegondi, Michael Kawczynski, (unknown), Simon S. Gao	2
6	Predicting geographic atrophy growth rate from fundus autofluorescence images using deep neural networks 2021 ·Neha Anegondi, Qi Yang, Michael Kawczynski, (unknown), Simon S. Gao	4
7	Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography 2020 ·Translational Vision Science & Technology ·Michael Kawczynski, Thomas Bengtsson, Jian S. Dai, J. Jill Hopkins, Simon S. Gao, Jeffrey R. Willis	29
8	Geographic Atrophy Lesion Segmentation Using a Deep Learning Network (U-net) 2019 ·Investigative Ophthalmology & Visual Science ·(unknown), Michael Kawczynski, Simon S. Gao, (unknown)	1
9	A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using ¹⁸F-FDG PET of the Brain breakdown → 2018 ·Radiology ·Yiming Ding, Jae Ho Sohn, Michael Kawczynski, Hari Trivedi, Roy Harnish, Nathaniel W. Jenkins, Dmytro Lituiev, Timothy P. Copeland, Mariam Aboian, Carina Marí Aparici, Spencer C. Behr, Robert R. Flavell, Shih-Ying Huang, Kelly A. Zalocusky, Lorenzo Nardo, Youngho Seo, Randall A. Hawkins, Miguel Hernandez Pampaloni, Dexter Hadley, Benjamin L. Franc	379
10	Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers 2018 ·Journal of Digital Imaging ·Dmytro Lituiev, Hari Trivedi, Maryam Panahiazar, Beau Norgeot, Youngho Seo, Benjamin L. Franc, Roy Harnish, Michael Kawczynski, Dexter Hadley	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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