José Maurício

524 total citations · 1 hit paper
5 papers, 283 citations indexed

About

José Maurício is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications. According to data from OpenAlex, José Maurício has authored 5 papers receiving a total of 283 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Radiology, Nuclear Medicine and Imaging and 1 paper in Computer Networks and Communications. Recurrent topics in José Maurício's work include Imbalanced Data Classification Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). José Maurício is often cited by papers focused on Imbalanced Data Classification Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). José Maurício collaborates with scholars based in Portugal. José Maurício's co-authors include Inês Domingues, Jorge Bernardino and Gonçalo Marques and has published in prestigious journals such as Applied Sciences and Pattern Analysis and Applications.

In The Last Decade

José Maurício

4 papers receiving 273 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.

Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review

2023 270 citations José Maurício, Inês Domingues et al. Applied Sciences profile →

Peers

José Maurício

AI

CVPR

RNMI

PS

BE

Brett Koonce

Abdul Rehman Khan Pakistan

Yurong Guo China

Zunaira Rauf Pakistan

Farhana Sultana India

Philomina Simon India

Xinwei He China

Shakhnoza Muksimova South Korea

Boxiong Yang China

Brett Koonce

José Maurício

64 ×0.8

AI

104 ×1.4

CVPR

31 ×0.7

RNMI

32 ×1.1

PS

24 ×0.9

BE

Citations per year, relative to José Maurício José Maurício (= 1×) peers Brett Koonce

Countries citing papers authored by José Maurício

Since Specialization

Citations

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

Fields of papers citing papers by José Maurício

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of José Maurício

This figure shows the co-authorship network connecting the top 25 collaborators of José Maurício. A scholar is included among the top collaborators of José Maurício 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 José Maurício. José Maurício is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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5 of 5 papers shown

1.

Maurício, José & Inês Domingues. (2024). Correction to: Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability. Pattern Analysis and Applications. 27(2).

2.

Maurício, José & Inês Domingues. (2024). Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability. Pattern Analysis and Applications. 27(1). 5 indexed citations

3.

Maurício, José, Inês Domingues, & Jorge Bernardino. (2023). Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review. Applied Sciences. 13(9). 5521–5521. 270 indexed citations breakdown →

4.

Maurício, José, et al.. (2021). Oversampling Techniques for Diabetes Classification: a Comparative Study. 1–6. 7 indexed citations

5.

Maurício, José & Gonçalo Marques. (2021). Predicting the Performance of Mathematics’ Students through Data Mining Techniques for Enhanced Education Systems. 3. 146–152. 1 indexed citations

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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