Ángel Molina

1.1k total citations
28 papers, 647 citations indexed

About

Ángel Molina is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Physiology. According to data from OpenAlex, Ángel Molina has authored 28 papers receiving a total of 647 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 7 papers in Physiology. Recurrent topics in Ángel Molina's work include Digital Imaging for Blood Diseases (12 papers), AI in cancer detection (7 papers) and Clinical Laboratory Practices and Quality Control (5 papers). Ángel Molina is often cited by papers focused on Digital Imaging for Blood Diseases (12 papers), AI in cancer detection (7 papers) and Clinical Laboratory Practices and Quality Control (5 papers). Ángel Molina collaborates with scholars based in Spain, Colombia and Argentina. Ángel Molina's co-authors include Anna Merino, José Rodellar, Andrea Acevedo, Santiago Alférez, Laura Boldú, José Antonio García‐Donaire, J.M. Morales, Amado Andrés, José Luis Bedini and Joan Carles Reverter and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Clinical Pathology and Clinica Chimica Acta.

In The Last Decade

Ángel Molina

27 papers receiving 630 citations

Peers

Ángel Molina

CVPR

RNMI

BIOPH

Andrea Acevedo Spain

Laura Boldú Spain

Noorul Wahab Egypt

Md Shahin Ali Bangladesh

Manu Goyal United Kingdom

Azamossadat Hosseini Iran

Harshita Sharma United Kingdom

Rashmi Mukherjee India

Bhavna Antony United States

Woohyung Lim South Korea

Andrea Acevedo Spain

Ángel Molina

500 ×1.2

CVPR

341 ×1.2

212 ×1.2

RNMI

132 ×1.1

BIOPH

75 ×1.2

Citations per year, relative to Ángel Molina Ángel Molina (= 1×) peers Andrea Acevedo

Countries citing papers authored by Ángel Molina

Since Specialization

Citations

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

Fields of papers citing papers by Ángel Molina

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ángel Molina

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

All Works

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20 of 20 papers shown

Merino, Anna, et al.. (2023). Kimura disease as an uncommon cause of persistent hypereosinophilia: a diagnostic challenge. Biochemia Medica. 33(2). 181–186. 2 indexed citations

Merino, Anna, et al.. (2022). Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan). Computer Methods and Programs in Biomedicine. 229. 107314–107314. 35 indexed citations

Boldú, Laura, et al.. (2022). Serous body fluid evaluation using the new automated haematology analyser Mindray BC-6800Plus. Clinical Chemistry and Laboratory Medicine (CCLM). 60(11). 1786–1795. 1 indexed citations

Rodellar, José, et al.. (2022). A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection. Bioengineering. 9(5). 229–229. 6 indexed citations

Acevedo, Andrea, Anna Merino, Laura Boldú, et al.. (2021). A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes. Computers in Biology and Medicine. 134. 104479–104479. 24 indexed citations

Molina, Ángel, et al.. (2021). Immune-Mediated Cutaneous Paraneoplastic Syndromes Associated With Hematologic Malignancies: Skin as a Mirror of Hematologic Neoplasms. Cureus. 13(11). e19538–e19538. 1 indexed citations

Molina, Ángel, José Rodellar, Laura Boldú, et al.. (2021). Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks. Computers in Biology and Medicine. 136. 104680–104680. 38 indexed citations

Boldú, Laura, Anna Merino, Andrea Acevedo, Ángel Molina, & José Rodellar. (2021). A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. Computer Methods and Programs in Biomedicine. 202. 105999–105999. 79 indexed citations

García‐Villoria, Judit, Antònia Ribes, Laura Gort, et al.. (2021). External quality assessment in the absence of proficiency testing: A split-sample testing program experience. Clinical Biochemistry. 97. 78–81. 2 indexed citations

10.

Cid, Joan, et al.. (2020). Hemolysis in the plasma of a specimen? Add a drop to clarify it. Transfusion. 60(12). 2783–2784. 2 indexed citations

11.

Molina, Ángel, Santiago Alférez, Laura Boldú, et al.. (2020). Sequential classification system for recognition of malaria infection using peripheral blood cell images. Journal of Clinical Pathology. 73(10). 665–670. 28 indexed citations

12.

Soler, Anna, Ángel Molina, José Luis Marı́n, et al.. (2020). Analytical performance specifications based on the state-of-the-art for the newborn screening. Clinica Chimica Acta. 510. 117–120. 1 indexed citations

13.

Acevedo, Andrea, Anna Merino, Santiago Alférez, et al.. (2020). A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. SHILAP Revista de lepidopterología. 30. 105474–105474. 159 indexed citations

14.

Merino, Anna, Alexandru Vlagea, Ángel Molina, et al.. (2020). Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value. Journal of Clinical Pathology. 75(2). 104–111. 11 indexed citations

15.

Molina, Ángel, et al.. (2020). A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection. Entropy. 22(6). 657–657. 50 indexed citations

16.

Merino, Anna, Ángel Molina, Rebecca Vicente‐Steijn, et al.. (2020). Detection and significance of green inclusions in peripheral blood neutrophils and monocytes. International Journal of Laboratory Hematology. 43(2). e92–e94. 4 indexed citations

17.

Boldú, Laura, Anna Merino, Santiago Alférez, et al.. (2019). Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis. Journal of Clinical Pathology. 72(11). 755–761. 40 indexed citations

18.

Rodellar, José, Santiago Alférez, Andrea Acevedo, Ángel Molina, & Anna Merino. (2018). Image processing and machine learning in the morphological analysis of blood cells. International Journal of Laboratory Hematology. 40(S1). 46–53. 80 indexed citations

19.

Molina, Ángel, et al.. (2005). Síndrome pneumo-renal en paciente con vasculitis de Schönlein-Henoch. Anales de Medicina Interna. 22(9). 441–4. 4 indexed citations

20.

García‐Donaire, José Antonio, et al.. (2005). Pregnancy in Renal Transplant Recipients. Transplantation Proceedings. 37(9). 3721–3722. 34 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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