Manu Goyal

1.9k total citations · 1 hit paper
18 papers, 1.1k citations indexed

About

Manu Goyal is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Manu Goyal has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 8 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Oncology. Recurrent topics in Manu Goyal's work include AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cutaneous Melanoma Detection and Management (6 papers). Manu Goyal is often cited by papers focused on AI in cancer detection (8 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and Cutaneous Melanoma Detection and Management (6 papers). Manu Goyal collaborates with scholars based in United Kingdom, United States and Sudan. Manu Goyal's co-authors include Moi Hoon Yap, Saeed Hassanpour, Shaofeng Yan, Thomas Knackstedt, Neil D. Reeves, Satyan Rajbhandari, Amanda Oakley, Darren Dancey, Reyer Zwiggelaar and Robert Martí and has published in prestigious journals such as SHILAP Revista de lepidopterología, American Journal Of Pathology and IEEE Access.

In The Last Decade

Manu Goyal

17 papers receiving 1.0k 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.

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities

2020 254 citations Manu Goyal, Thomas Knackstedt et al. Computers in Biology and Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Manu Goyal United Kingdom	11	541	425	279	239	216	18	1.1k
Amirreza Mahbod Austria	10	549 1.0×	548 1.3×	136 0.5×	47 0.2×	232 1.1×	19	870
Irene Fondón Spain	16	408 0.8×	526 1.2×	250 0.9×	6 0.0×	148 0.7×	32	936
Keerthana Prasad India	21	516 1.0×	74 0.2×	489 1.8×	27 0.1×	94 0.4×	74	1.3k
Guilian Chen China	7	234 0.4×	167 0.4×	96 0.3×	9 0.0×	75 0.3×	19	488
Ahmad Naeem Pakistan	13	388 0.7×	329 0.8×	193 0.7×	9 0.0×	152 0.7×	25	680
Catarina Barata Portugal	18	900 1.7×	1.0k 2.4×	112 0.4×	9 0.0×	281 1.3×	40	1.4k
Woohyung Lim South Korea	7	522 1.0×	590 1.4×	130 0.5×	3 0.0×	243 1.1×	14	933
Yading Yuan United States	12	503 0.9×	321 0.8×	314 1.1×	5 0.0×	130 0.6×	37	971
Tao Chen China	22	145 0.3×	163 0.4×	226 0.8×	7 0.0×	53 0.2×	113	1.6k
Jun Shi China	14	53 0.1×	46 0.1×	87 0.3×	18 0.1×	48 0.2×	53	517

Countries citing papers authored by Manu Goyal

Since Specialization

Citations

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

Fields of papers citing papers by Manu Goyal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manu Goyal

This figure shows the co-authorship network connecting the top 25 collaborators of Manu Goyal. A scholar is included among the top collaborators of Manu Goyal 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 Manu Goyal. Manu Goyal 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:

18 of 18 papers shown

Goyal, Manu, Jonathan D. Marotti, Graham M. Tooker, et al.. (2024). A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk. npj Breast Cancer. 10(1). 93–93. 7 indexed citations

Goyal, Manu, et al.. (2024). Deep Learning for Grading Endometrial Cancer. American Journal Of Pathology. 194(9). 1701–1711. 8 indexed citations

Guo, Junyu, et al.. (2023). Style Transfer–assisted Deep Learning Method for Kidney Segmentation at Multiphase MRI. Radiology Artificial Intelligence. 5(6). e230043–e230043. 5 indexed citations

Abdollahi, Behnaz, Manu Goyal, Matthew A. Suriawinata, et al.. (2022). Bladder cancer prognosis using deep neural networks and histopathology images. Journal of Pathology Informatics. 13. 100135–100135. 19 indexed citations

Goyal, Manu, et al.. (2022). Automated kidney segmentation by mask R-CNN in T2-weighted magnetic resonance imaging. 9 indexed citations

Kansagra, Akash P., et al.. (2022). Performance of Automated RAPID Intracranial Hemorrhage Detection in Real-World Practice: A Single-Institution Experience. Journal of Computer Assisted Tomography. 46(5). 770–774. 7 indexed citations

Tabatabaei, Mohsen, et al.. (2021). Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.. SHILAP Revista de lepidopterología. 46(6). 420–427. 3 indexed citations

Goyal, Manu, Neil D. Reeves, Satyan Rajbhandari, et al.. (2020). Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques. Computers in Biology and Medicine. 117. 103616–103616. 139 indexed citations

Goyal, Manu, Thomas Knackstedt, Shaofeng Yan, & Saeed Hassanpour. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Computers in Biology and Medicine. 127. 104065–104065. 254 indexed citations breakdown →

10.

Yap, Moi Hoon, Manu Goyal, Fatima Osman, et al.. (2020). Breast ultrasound region of interest detection and lesion localisation. Artificial Intelligence in Medicine. 107. 101880–101880. 99 indexed citations

11.

Goyal, Manu, Moi Hoon Yap, & Saeed Hassanpour. (2020). Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks. 290–295. 12 indexed citations

12.

Yap, Moi Hoon, et al.. (2019). Skin lesion boundary segmentation with fully automated deep extreme cut methods. ResearchSpace (University of Auckland). 24–24. 10 indexed citations

13.

Yap, Moi Hoon, et al.. (2019). The effect of color constancy algorithms on semantic segmentation of skin lesions. 25–25. 21 indexed citations

14.

Goyal, Manu. (2019). Natural Data-augmentation for Skin Lesions (ISIC-2017 Challenge Dataset). Data Archiving and Networked Services (DANS). 1. 1 indexed citations

15.

Goyal, Manu, et al.. (2019). Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods. IEEE Access. 8. 4171–4181. 219 indexed citations

16.

Yap, Moi Hoon, Manu Goyal, Fatima Osman, et al.. (2018). Breast ultrasound lesions recognition: end-to-end deep learning approaches. Journal of Medical Imaging. 6(1). 1–1. 92 indexed citations

17.

Goyal, Manu, Neil D. Reeves, Satyan Rajbhandari, & Moi Hoon Yap. (2018). Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices. IEEE Journal of Biomedical and Health Informatics. 23(4). 1730–1741. 134 indexed citations

18.

Osman, Fatima, Robert Martí, Reyer Zwiggelaar, et al.. (2018). End-to-end breast ultrasound lesions recognition with a deep learning approach. 7259. 44–44. 24 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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