Ashnil Kumar

2.9k total citations · 1 hit paper
54 papers, 1.8k citations indexed

About

Ashnil Kumar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ashnil Kumar has authored 54 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 14 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ashnil Kumar's work include AI in cancer detection (20 papers), Image Retrieval and Classification Techniques (18 papers) and Medical Image Segmentation Techniques (11 papers). Ashnil Kumar is often cited by papers focused on AI in cancer detection (20 papers), Image Retrieval and Classification Techniques (18 papers) and Medical Image Segmentation Techniques (11 papers). Ashnil Kumar collaborates with scholars based in Australia, China and Hong Kong. Ashnil Kumar's co-authors include Jinman Kim, Michael Fulham, Dagan Feng, Lei Bi, Euijoon Ahn, Dagan Feng, Jinman Kim, Weidong Cai, Changyang Li and Lingfeng Wen and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging.

In The Last Decade

Ashnil Kumar

53 papers receiving 1.7k citations

Hit Papers

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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.

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification

2016 382 citations Ashnil Kumar, Jinman Kim et al. IEEE Journal of Biomedical and Health Informatics profile →

Peers

Countries citing papers authored by Ashnil Kumar

Since Specialization

Citations

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

Fields of papers citing papers by Ashnil Kumar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashnil Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Ashnil Kumar. A scholar is included among the top collaborators of Ashnil Kumar 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 Ashnil Kumar. Ashnil Kumar 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

#	Work	Indexed citations
1	An artificial intelligence algorithm for the classification of sphenoid sinus pneumatisation on sinus computed tomography scans 2023 ·Clinical Otolaryngology ·(unknown), Al‐Rahim Habib, Ashnil Kumar, Eugene Wong, Zubair Hasan, Narinder Singh	6
2	Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis 2022 ·Clinical Otolaryngology ·Al‐Rahim Habib, (unknown), Zubair Hasan, Eugene Wong, Hasantha Gunasekera, Chris Perry, Raymond Sacks, Ashnil Kumar, Narinder Singh	29
3	Convolutional Neural Networks in ENT Radiology: Systematic Review of the Literature 2022 ·Annals of Otology Rhinology & Laryngology ·Zubair Hasan, (unknown), Al‐Rahim Habib, Eugene Wong, (unknown), Ashnil Kumar, Raymond Sacks, Narinder Singh	11
4	Fused feature signatures to probe tumour radiogenomics relationships 2022 ·Scientific Reports ·Tian Xia, Ashnil Kumar, Michael Fulham, Dagan Feng, Yue Wang, Eun Young Kim, Younhyun Jung, Jinman Kim	4
5	Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning 2021 ·arXiv (Cornell University) ·(unknown), Lei Bi, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim	3
6	Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation 2020 ·IEEE Transactions on Medical Imaging ·Euijoon Ahn, Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim	38
7	Unsupervised Deep Transfer Feature Learning for Medical Image Classification 2019 ·Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim	27
8	An Automated Framework for Large Scale Retrospective Analysis of Ultrasound Images 2019 ·IEEE Journal of Translational Engineering in Health and Medicine ·(unknown), Ashnil Kumar, Ann Quinton, (unknown), Ralph Nanan, Jinman Kim	3
9	Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks 2019 ·Ultrasound in Medicine & Biology ·(unknown), Ashnil Kumar, Ann Quinton, Ralph Nanan, Jinman Kim, Ramarathnam Krishna Kumar	50
10	Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns 2018 ·IEEE Transactions on Medical Imaging ·(unknown), Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim	13
11	Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images 2017 ·IEEE Journal of Biomedical and Health Informatics ·Euijoon Ahn, Jinman Kim, Lei Bi, Ashnil Kumar, Changyang Li, Michael Fulham, Dagan Feng	110
12	Neural Captioning for the ImageCLEF 2017 Medical Image Challenges. 2017 ·CLEF (Working Notes) ·(unknown), Ashnil Kumar, Jinman Kim	12
13	Subfigure and Multi-Label Classification using a Fine-Tuned Convolutional Neural Network. 2016 ·CLEF (Working Notes) ·Ashnil Kumar, (unknown), Jinman Kim, Dagan Feng	10
14	Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram 2016 ·Computerized Medical Imaging and Graphics ·Younhyun Jung, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham	7
15	Adapting content-based image retrieval techniques for the semantic annotation of medical images 2016 ·Computerized Medical Imaging and Graphics ·Ashnil Kumar, (unknown), Jinman Kim, Changyang Li, Philip H. W. Leong, Michael Fulham, Dagan Feng	37
16	Convolutional Neural Networks for Medical Clustering 2015 ·CLEF (Working Notes) ·(unknown), Ashnil Kumar, Jinman Kim, Philip H. W. Leong, Dagan Feng	11
17	Convolutional Neural Networks for Subfigure Classification. 2015 ·CLEF (Working Notes) ·(unknown), Ashnil Kumar, Jinman Kim, Philip H. W. Leong, Dagan Feng	4
18	Automatic Annotation of Liver CT Images: the Submission of the BMET Group to ImageCLEFmed 2014 2014 ·CLEF (Working Notes) ·Ashnil Kumar, (unknown), Changyang Li, Philip H. W. Leong, Jinman Kim	5
19	Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data 2013 ·Journal of Digital Imaging ·Ashnil Kumar, Jinman Kim, Weidong Cai, Michael Fulham, Dagan Feng	156
20	A web-based image viewer for multiple PET-CT follow-up studies 2011 ·PubMed ·(unknown), Jinman Kim, Ashnil Kumar, (unknown), (unknown), Dagan Feng	2

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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