Anabik Pal

710 total citations
25 papers, 462 citations indexed

About

Anabik Pal is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Gastroenterology. According to data from OpenAlex, Anabik Pal has authored 25 papers receiving a total of 462 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Gastroenterology. Recurrent topics in Anabik Pal's work include AI in cancer detection (7 papers), Gastroesophageal reflux and treatments (5 papers) and Digital Imaging for Blood Diseases (4 papers). Anabik Pal is often cited by papers focused on AI in cancer detection (7 papers), Gastroesophageal reflux and treatments (5 papers) and Digital Imaging for Blood Diseases (4 papers). Anabik Pal collaborates with scholars based in India, United States and Switzerland. Anabik Pal's co-authors include Utpal Garain, Raghunath Chatterjee, Aditi Chandra, Dhirendra S. Katti, Swapan Senapati, Praveen Kumar, Zhiyun Xue, Mark Fox, Sameer Antani and Anandarup Roy and has published in prestigious journals such as IEEE Access, Acta Biomaterialia and Pattern Recognition.

In The Last Decade

Anabik Pal

25 papers receiving 451 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Anabik Pal India 13 124 114 98 73 73 25 462
Wenhui Lei China 15 68 0.5× 25 0.2× 49 0.5× 111 1.5× 49 0.7× 38 503
Zhekai Hu China 13 35 0.3× 14 0.1× 67 0.7× 16 0.2× 17 0.2× 27 401
Shih‐Che Huang Taiwan 15 41 0.3× 39 0.3× 160 1.6× 5 0.1× 55 0.8× 69 886
Yufan Wu China 18 12 0.1× 19 0.2× 90 0.9× 8 0.1× 70 1.0× 74 728
Junseok Park South Korea 13 9 0.1× 175 1.5× 111 1.1× 18 0.2× 101 1.4× 46 642
Kyung Uk Jung South Korea 14 19 0.2× 61 0.5× 277 2.8× 106 1.5× 236 3.2× 55 671
Araki Japan 10 12 0.1× 25 0.2× 71 0.7× 5 0.1× 51 0.7× 34 477
Guoxin Zhang China 17 10 0.1× 215 1.9× 643 6.6× 45 0.6× 53 0.7× 56 939
Tsung‐Po Chen Taiwan 11 84 0.7× 19 0.2× 70 0.7× 55 0.8× 13 0.2× 23 530
Yisen Huang China 10 9 0.1× 26 0.2× 64 0.7× 49 0.7× 27 0.4× 41 294

Countries citing papers authored by Anabik Pal

Since Specialization
Citations

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

Fields of papers citing papers by Anabik Pal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anabik Pal

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

All Works

20 of 20 papers shown
1.
Agrawal, Ankur & Anabik Pal. (2025). Adaptive Hybrid Genetic-Ant Colony Optimization for Dynamic Self-Healing and Network Performance Optimization in 5G/6G Networks. Procedia Computer Science. 252. 404–413. 2 indexed citations
2.
Pal, Anabik & Sukalyan Chakraborty. (2025). Hidden hazards: microplastics in intravenous admixtures and their path into the body. Environmental Monitoring and Assessment. 197(4). 400–400. 3 indexed citations
3.
Pal, Anabik, Zhiyun Xue, & Sameer Antani. (2023). Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets. Lecture notes in networks and systems. 519. 679–688. 2 indexed citations
4.
Pal, Anabik, et al.. (2023). Federated learning using multi-institutional data for generalizable chest x-ray diagnosis. 33. 15–15. 1 indexed citations
5.
Xue, Zhiyun, Paul C. Pearlman, Kelly J. Yu, et al.. (2022). Oral cavity anatomical site image classification and analysis. PubMed. 12037. 13–13. 7 indexed citations
6.
Xue, Zhiyun, Kelly J. Yu, Paul C. Pearlman, et al.. (2022). Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 3218–3221. 12 indexed citations
7.
Pal, Anabik, Zhiyun Xue, Brian Befano, et al.. (2021). Deep Metric Learning for Cervical Image Classification. IEEE Access. 9. 53266–53275. 31 indexed citations
8.
Pal, Anabik, Aditi Chandra, Raghunath Chatterjee, et al.. (2021). MICaps: Multi-instance capsule network for machine inspection of Munro's microabscess. Computers in Biology and Medicine. 140. 105071–105071. 3 indexed citations
9.
Xue, Zhiyun, Peng Guo, Kanan Desai, et al.. (2021). A Deep Clustering Method For Analyzing Uterine Cervix Images Across Imaging Devices. PubMed. 2021. 527–532. 7 indexed citations
10.
Pal, Anabik, Zhiyun Xue, Kanan Desai, et al.. (2021). Deep multiple-instance learning for abnormal cell detection in cervical histopathology images. Computers in Biology and Medicine. 138. 104890–104890. 38 indexed citations
11.
Pal, Anabik, Utpal Garain, Aditi Chandra, Raghunath Chatterjee, & Swapan Senapati. (2018). Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network. Computer Methods and Programs in Biomedicine. 159. 59–69. 59 indexed citations
12.
Roy, Anandarup, Anabik Pal, & Utpal Garain. (2017). JCLMM: A finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation. Pattern Recognition. 66. 160–173. 28 indexed citations
13.
Parker, Helen L., Emily Tucker, Caroline L. Hoad, et al.. (2016). Development and validation of a large, modular test meal with liquid and solid components for assessment of gastric motor and sensory function by non‐invasive imaging. Neurogastroenterology & Motility. 28(4). 554–568. 22 indexed citations
14.
Pal, Anabik, Anandarup Roy, Kushal Sen, et al.. (2015). Mixture model based color clustering for psoriatic plaque segmentation. 376–380. 7 indexed citations
15.
Pal, Anabik, Utpal Garain, Raghunath Chatterjee, & Swapan Senapati. (2015). Psoriatic plaque segmentation in skin images. 18. 1–4. 5 indexed citations
16.
Das, Nibaran, et al.. (2013). An SVM Based Skin Disease Identification Using Local Binary Patterns. 208–211. 21 indexed citations
17.
Kumar, Praveen, et al.. (2012). Mathematical model of mechanical behavior of micro/nanofibrous materials designed for extracellular matrix substitutes. Acta Biomaterialia. 8(11). 4111–4122. 59 indexed citations
18.
Roy, Samapriya, Mark Fox, Jelena Curcic, Werner Schwizer, & Anabik Pal. (2012). The gastro‐esophageal reflux barrier: biophysical analysis on 3D models of anatomy from magnetic resonance imaging. Neurogastroenterology & Motility. 24(7). 616–616. 17 indexed citations
19.
Kwiatek, M. A., Mark Fox, Andreas Steingoetter, et al.. (2009). Effects of clonidine and sumatriptan on postprandial gastric volume response, antral contraction waves and emptying: an MRI study. Neurogastroenterology & Motility. 21(9). 928–928. 25 indexed citations
20.
Banerjee, Mithu, et al.. (1990). Small intestinal involvement in visceral leishmaniasis.. PubMed. 85(10). 1433–4. 7 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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