Philip Chikontwe

707 total citations
26 papers, 320 citations indexed

About

Philip Chikontwe is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Philip Chikontwe has authored 26 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 14 papers in Artificial Intelligence and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Philip Chikontwe's work include AI in cancer detection (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Philip Chikontwe is often cited by papers focused on AI in cancer detection (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Philip Chikontwe collaborates with scholars based in South Korea, United States and China. Philip Chikontwe's co-authors include Sang Hyun Park, Soopil Kim, Miguel A. Cabra de Luna, Hyo Jong Lee, June Hong Ahn, Kyung Soo Hong, Weili Lin, Dinggang Shen, Xiaopeng Zong and Heounjeong Go and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Philip Chikontwe

25 papers receiving 319 citations

Peers

Philip Chikontwe

CVPR

RNMI

Xufeng Yao China

Steven McDonagh United Kingdom

Kamalanand Krishnamurthy India

Amira Echtioui Tunisia

Gerard Sanromà Spain

Chaoqing Wang China

Gökhan Altan Türkiye

Parisa Gifani Iran

Tomás Teijeiro Switzerland

Xufeng Yao China

Philip Chikontwe

176 ×1.2

192 ×1.6

CVPR

92 ×1.1

RNMI

24 ×0.5

25 ×0.8

Citations per year, relative to Philip Chikontwe Philip Chikontwe (= 1×) peers Xufeng Yao

Countries citing papers authored by Philip Chikontwe

Since Specialization

Citations

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

Fields of papers citing papers by Philip Chikontwe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philip Chikontwe

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Kim, Soopil, Hee Jung Park, Philip Chikontwe, et al.. (2025). Communication Efficient Federated Learning for Multi-Organ Segmentation via Knowledge Distillation With Image Synthesis. IEEE Transactions on Medical Imaging. 44(5). 2079–2092.

Chikontwe, Philip, et al.. (2024). Video domain adaptation for semantic segmentation using perceptual consistency matching. Neural Networks. 179. 106505–106505. 6 indexed citations

Kim, Soopil, et al.. (2024). Few-shot anomaly detection using positive unlabeled learning with cycle consistency and co-occurrence features. Expert Systems with Applications. 256. 124890–124890. 5 indexed citations

Chikontwe, Philip, Meejeong Kim, Jaehoon Jeong, et al.. (2024). FR-MIL: Distribution Re-Calibration-Based Multiple Instance Learning With Transformer for Whole Slide Image Classification. IEEE Transactions on Medical Imaging. 44(1). 409–421. 5 indexed citations

Luna, Miguel A. Cabra de, et al.. (2024). Attention guided multi-scale cluster refinement with extended field of view for amodal nuclei segmentation. Computers in Biology and Medicine. 170. 108015–108015. 5 indexed citations

Luna, Miguel A. Cabra de, Philip Chikontwe, & Sang Hyun Park. (2024). Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model. Bioengineering. 11(3). 294–294. 1 indexed citations

Kim, Soopil, et al.. (2024). Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 38(8). 8591–8599. 7 indexed citations

Kim, Soopil, et al.. (2023). Uncertainty-aware semi-supervised few shot segmentation. Pattern Recognition. 137. 109292–109292. 14 indexed citations

Won, Dongkyu, Miguel A. Cabra de Luna, Philip Chikontwe, et al.. (2023). Content preserving image translation with texture co-occurrence and spatial self-similarity for texture debiasing and domain adaptation. Neural Networks. 166. 722–737. 5 indexed citations

10.

Chikontwe, Philip, Soopil Kim, Kyong Hwan Jin, et al.. (2023). One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation. Lecture notes in computer science. 14221. 521–531. 5 indexed citations

11.

Kim, Soopil, et al.. (2023). Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 15479–15493. 24 indexed citations

12.

Chikontwe, Philip, et al.. (2023). Structure-Preserving Image Translation for Cross-Modality Medical Imaging Domain Adaptation. SSRN Electronic Journal. 1 indexed citations

13.

Chikontwe, Philip, Jaehoon Jeong, Meejeong Kim, et al.. (2022). Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization. Medical Image Analysis. 80. 102482–102482. 15 indexed citations

14.

Chikontwe, Philip, Soopil Kim, & Sang Hyun Park. (2022). CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14534–14543. 30 indexed citations

15.

Hong, Kyung Soo, Philip Chikontwe, Miguel A. Cabra de Luna, et al.. (2021). Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective. Journal of Korean Medical Science. 36(5). e46–e46. 17 indexed citations

16.

Chikontwe, Philip, et al.. (2021). Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening. Medical Image Analysis. 72. 102105–102105. 33 indexed citations

17.

Chikontwe, Philip, et al.. (2021). Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation. Applied Sciences. 11(4). 1638–1638. 4 indexed citations

18.

Chikontwe, Philip, et al.. (2019). Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network. IEEE Access. 7. 18382–18391. 24 indexed citations

19.

Chikontwe, Philip, et al.. (2019). Real-Time Tracking of Guidewire Robot Tips Using Deep Convolutional Neural Networks on Successive Localized Frames. IEEE Access. 7. 159743–159753. 15 indexed citations

20.

Chikontwe, Philip & Hyo Jong Lee. (2018). Deep Multi-Task Network for Learning Person Identity and Attributes. IEEE Access. 6. 60801–60811. 16 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.

Explore authors with similar magnitude of impact