| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 18 | 404 | 865 | 91 |
Medical Image Analysis is organized to address concerns in the fields of Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Deep learning. The study on Artificial intelligence presented in Medical Image Analysis intersects with the topics under Machine learning. Imaging phantom, Algorithm and Robustness (computer science) are some topics wherein Computer vision research discussed in it have an impact.
Medical Image Analysis connects the study in Algorithm with the closely related area of Diffusion MRI. While Pattern recognition is the focus of the journal, it also provided insights into the studies of Artificial neural network, Similarity (geometry) and Feature (computer vision). Segmentation research featured in Medical Image Analysis incorporates concerns from various other topics such as Pixel, Ground truth, Magnetic resonance imaging and Medical imaging.
Segmentation-based object categorization is a major topic of Scale-space segmentation research.
The most cited papers are organized to reinforce research efforts on Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Image processing. The journal publications connects research in Artificial intelligence with the related topics of Machine learning. The most cited articles hold forums on Computer vision that merge themes from other disciplines such as Algorithm, Tomography and Medical imaging.
Medical Image Analysis generally zeroes in on subjects such as Artificial intelligence, Deep learning, Pattern recognition, Computer vision and Segmentation. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Dimension (vector space), Encoder and Distortion (mathematics). The close relationship between Scattering and Image (mathematics) and RGB color model is one of the points of interest dissected in Dimension (vector space) research.
Encoder research presented in Medical Image Analysis encompasses a variety of subjects, including Orientation (computer vision) and Vertebra. The Data set works featured in the journal incorporate elements from Motion (physics), Similarity (geometry), Tracking (particle physics), Tracking error and Image-guided radiation therapy. The concepts on Convolutional neural network presented in it can also apply to other research fields, including Artificial neural network, Intravascular ultrasound, Ground truth, Lumen (anatomy) and Hausdorff distance.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Medical Image Analysis (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Medical Image Analysis (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2022 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 0.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 40.00% of all publications and 60.00% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
In expanding the application of Medical Image Analysis, it's essential to consider its implications in the field of education, particularly in programs that teach future clinicians, technologists, and research professionals. More specifically, the knowledge gained from studying medical imaging technologies and techniques can guide educators in structuring curriculums and teaching methods that work best to impart these skills. For instance, roles like art teachers often use a variety of media to teach students. Incorporating an understanding of medical image analysis could help these professionals design more effective, engaging lessons on visual art. It would allow them to draw connections between art and science, demonstrating how similar principles of visualization and pattern recognition apply across disciplines. Those interested in delving into how subjects, such as art, can make use of medical image analysis in teaching could consider pursuing specific trainings or career paths in this regard. One such path could be becoming an elementary art teacher specialized in using medical image analysis in New Hampshire, a role that blends expertise in art, technology and teaching. More on this career path, including the qualifications and training needed, can be found here. Like any other subject, integrating Medical Image Analysis into education requires careful consideration of the skills and knowledge required for each specific role. This challenge can be eased by seeking resources and training in relevant fields, illustrating how interdisciplinary knowledge can lead to innovative teaching methods in the classroom.
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(2022)Shervin Minaee;Rahele Kafieh;Milan Sonka;Shakib Yazdani
(2020)José Ignacio Orlando;Huazhu Fu;João Barbossa Breda;Karel van Keer
(2020)A. Emre Kavur;N. Sinem Gezer;Mustafa Barış;Sinem Aslan
(2021)Hoel Kervadec;Jihene Bouchtiba;Christian Desrosiers;Eric Granger
(2021)Davood Karimi;Haoran Dou;Simon K. Warfield;Ali Gholipour
(2020)Tanya Nair;Doina Precup;Douglas L. Arnold;Tal Arbel
(2020)Nicholas Heller;Fabian Isensee;Klaus H. Maier-Hein;Xiaoshuai Hou
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