Michael D. Heath

14 papers receiving 1.2k citations

Michael D. Heath's Hit Papers

THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY 2007 · 656 citations
6560+6+12Years since publication200400600

Peers

Michael D. Heath
Comparison fields: 5 of 101
  • Computer Vision and Pattern Recognition 789
  • Artificial Intelligence 633
  • Radiology, Nuclear Medicine and Imaging 379
  • Media Technology 139
  • Neurology 55
Replace Yuqian Zhao with:
Yuqian Zhao China
Mingfeng Jiang China
Hidefumi Kobatake Japan
Jiliu Zhou China
Shahriar B. Shokouhi Iran
Yongzhao Du China
Ezzeddine Zagrouba Tunisia
Siti Noraini Sulaiman Malaysia
Camel Tanougast France
Guanghui Yue China
Michael D. Heath relative to Yuqian Zhao China Yuqian Zhao's profile →
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Countries citing papers authored by Michael D. Heath

Since Specialization
Citations

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

Fields of papers citing papers by Michael D. Heath

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 22 scholars most cited alongside Michael D. Heath, 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 D. Heath Line = papers co-authored together Michael D. Heath links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY
Hit paper breakdown →
2007656
2 1997358
3 1996132
4 199834
5 201433
6 201223
7 201318
8 20069
9 20017
10 20147
11 20165
12 19985
13 20154
14 20094

About Michael D. Heath

Michael D. Heath is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Radiation and Biomedical Engineering, having authored 14 papers that have together received 1.3k indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (6 papers), Digital Radiography and Breast Imaging (5 papers), Advanced Radiotherapy Techniques (3 papers), Image and Object Detection Techniques (3 papers), Advanced X-ray and CT Imaging (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Medical Image Segmentation Techniques (2 papers) and Breast Cancer Treatment Studies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (789 citations), Artificial Intelligence (633 citations), Radiology, Nuclear Medicine and Imaging (379 citations), Media Technology (139 citations) and Neurology (55 citations). Michael D. Heath has collaborated with scholars based in United States and Canada. Frequent co-authors include Kevin W. Bowyer, Roscoe M. Moore, D B Kopans, Thomas Sanocki, Sudeep Sarkar, Aaron Cohen‐Gadol, Jing Shan, Jianping Lü, Yueh Z. Lee and David H. Foos. Their work appears in journals such as Journal of Experimental Psychology Human Perception & Performance, Physics in Medicine and Biology, IEEE Transactions on Pattern Analysis and Machine Intelligence, British Journal of Neurosurgery and International Journal of Pattern Recognition and Artificial Intelligence.

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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