Michael Pekala

15 papers receiving 562 citations

Michael Pekala's Hit Papers

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks 2017 · 426 citations
4260+3+6Years since publication100200300400

Peers

Michael Pekala
Comparison fields: 5 of 78
  • Ophthalmology 334
  • Health Informatics 25
  • Radiology, Nuclear Medicine and Imaging 406
  • Structural Biology 9
  • Computer Vision and Pattern Recognition 112
Replace Joaquim de Moura with:
Joaquim de Moura Spain
Rongchang Zhao China
Ling Dai China
Daniel Kermany United States
Yangqin Feng Singapore
Ali Serener Cyprus
Swamidoss Issac Niwas India
Paweł Liskowski Poland
Michael Pekala relative to Joaquim de Moura Spain Joaquim de Moura's profile →
Citations per field
00.5×
Joaquim de Moura · 1×
Citations per year

Countries citing papers authored by Michael Pekala

Since Specialization
Citations

This map shows the geographic impact of Michael Pekala'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 Pekala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Pekala more than expected).

Fields of papers citing papers by Michael Pekala

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Pekala. 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 Pekala. The network helps show where Michael Pekala may publish in the future.

Co-authors

The 25 scholars most cited alongside Michael Pekala, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Pekala Line = papers co-authored together Michael Pekala links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
Hit paper breakdown →
2017426
2 201866
3 201712
4
Model-Based Autonomy for the Next Generation of Robotic Spacecraft
200210
5 201510
6 201510
7 20239
8 20028
9 20078
10 20087
11 20194
12 20092
13 20121
14 20051
15 20111
16 20240

About Michael Pekala

Michael Pekala is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Ophthalmology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 16 papers that have together received 575 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (3 papers), Retinal and Optic Conditions (3 papers), AI-based Problem Solving and Planning (2 papers), Fault Detection and Control Systems (2 papers), Robotic Path Planning Algorithms (2 papers), Real-Time Systems Scheduling (2 papers), Distributed systems and fault tolerance (2 papers) and Retinal Diseases and Treatments (2 papers). The work is most often cited by research in Ophthalmology (334 citations), Health Informatics (25 citations), Radiology, Nuclear Medicine and Imaging (406 citations), Structural Biology (9 citations) and Computer Vision and Pattern Recognition (112 citations). Michael Pekala has collaborated with scholars based in United States. Frequent co-authors include Kátia D. Pacheco, David Freund, Neil M. Bressler, Philippe Burlina, Neil Joshi, Wojciech Czaja, I-Jeng Wang, R. Jacob Vogelstein, Randal Burns and Jun Kong. Their work appears in journals such as The Journal of Physical Chemistry C, Frontiers in Neuroinformatics, JAMA Ophthalmology, Lecture notes in computer science and IEEE Instrumentation & Measurement Magazine.

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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