Andrew Janowczyk

4.6k total citations · 1 hit paper
68 papers, 2.5k citations indexed

About

Andrew Janowczyk is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Andrew Janowczyk has authored 68 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 29 papers in Radiology, Nuclear Medicine and Imaging and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in Andrew Janowczyk's work include AI in cancer detection (44 papers), Radiomics and Machine Learning in Medical Imaging (24 papers) and Cell Image Analysis Techniques (15 papers). Andrew Janowczyk is often cited by papers focused on AI in cancer detection (44 papers), Radiomics and Machine Learning in Medical Imaging (24 papers) and Cell Image Analysis Techniques (15 papers). Andrew Janowczyk collaborates with scholars based in United States, Switzerland and India. Andrew Janowczyk's co-authors include Anant Madabhushi, Hannah Gilmore, Michael D. Feldman, Ajay Basavanhally, David Romo‐Bucheli, Cheng Lu, Xiangxue Wang, Sharat Chandran, Eduardo Romero and Jun Xu and has published in prestigious journals such as Circulation, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Andrew Janowczyk

63 papers receiving 2.5k 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.

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

2016 846 citations Andrew Janowczyk, Anant Madabhushi profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Andrew Janowczyk United States	24	1.7k	1.3k	687	390	387	68	2.5k
Allen P. Miraflor United States	7	1.3k 0.8×	944 0.8×	446 0.6×	200 0.5×	462 1.2×	11	1.8k
Ajay Basavanhally United States	19	1.8k 1.1×	1.1k 0.9×	1.0k 1.5×	395 1.0×	207 0.5×	28	2.3k
Korsuk Sirinukunwattana United Kingdom	16	1.6k 0.9×	1.1k 0.8×	969 1.4×	341 0.9×	413 1.1×	38	2.2k
Nicolas Coudray United States	17	1.2k 0.7×	1.1k 0.9×	258 0.4×	264 0.7×	495 1.3×	49	2.5k
John Tomaszewski United States	33	2.1k 1.2×	1.5k 1.2×	1.2k 1.8×	381 1.0×	379 1.0×	79	3.9k
Theodore Sakellaropoulos United States	14	1.1k 0.7×	994 0.8×	238 0.3×	228 0.6×	410 1.1×	30	2.5k
Meyke Hermsen Netherlands	13	1.1k 0.6×	734 0.6×	428 0.6×	198 0.5×	225 0.6×	22	1.6k
Nina Linder Finland	24	743 0.4×	633 0.5×	300 0.4×	184 0.5×	450 1.2×	57	2.1k
Catarina Eloy Portugal	24	863 0.5×	673 0.5×	327 0.5×	126 0.3×	413 1.1×	109	2.3k
Mohamed Amgad United States	18	692 0.4×	717 0.6×	195 0.3×	148 0.4×	195 0.5×	32	1.6k

Countries citing papers authored by Andrew Janowczyk

Since Specialization

Citations

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

Fields of papers citing papers by Andrew Janowczyk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrew Janowczyk

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

Grobholz, Rainer, Andrew Janowczyk, & Inti Zlobec. (2025). Transforming pathology into digital pathology: highway to hell or stairway to heaven?. Diagnostic histopathology. 31(7). 410–415. 1 indexed citations

Graham, Simon, et al.. (2024). HoverFast: an accurate, high-throughput, clinicallydeployable nuclear segmentation tool for brightfield digital pathologyimages. The Journal of Open Source Software. 9(101). 7022–7022. 1 indexed citations

Jagtap, Jaidip, Andrew Janowczyk, Yijiang Chen, et al.. (2024). Glomerular and Nephron Size and Kidney Disease Outcomes: A Comparison of Manual Versus Deep Learning Methods in Kidney Pathology. Kidney Medicine. 7(1). 100939–100939. 1 indexed citations

Chen, Yijiang, Fan Fan, Céline C. Berthier, et al.. (2024). Clinical Relevance of Computational Pathology Analysis of Interplay between Kidney Microvasculature and Interstitial Microenvironment. Clinical Journal of the American Society of Nephrology. 20(2). 239–255. 1 indexed citations

Bishop, Kevin W., Lindsey A. Barner, Qinghua Han, et al.. (2024). An end-to-end workflow for nondestructive 3D pathology. Nature Protocols. 19(4). 1122–1148. 9 indexed citations

Koyuncu, Can, Andrew Janowczyk, Xavier Farré, et al.. (2023). Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity. Laboratory Investigation. 103(12). 100265–100265. 3 indexed citations

Serafin, Robert, Can Koyuncu, Weisi Xie, et al.. (2023). Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment. The Journal of Pathology. 260(4). 390–401. 13 indexed citations

Liu, Jonathan, Richard Colling, Michelle R. Downes, et al.. (2023). Engineering the future of 3D pathology. The Journal of Pathology Clinical Research. 10(1). e347–e347. 6 indexed citations

Chen, Yijiang, Jarcy Zee, Andrew Janowczyk, et al.. (2023). Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies. Kidney360. 4(5). 648–658. 13 indexed citations

10.

Qiao, Jiangtao, Andrew Janowczyk, Suresh Poudel, et al.. (2022). Meta-omics-aided isolation of an elusive anaerobic arsenic-methylating soil bacterium. The ISME Journal. 16(7). 1740–1749. 48 indexed citations

11.

Bhargava, Hersh K., Patrick Leo, Robin Elliott, et al.. (2020). Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clinical Cancer Research. 26(8). 1915–1923. 42 indexed citations

12.

Dong, Yuan, Suresh Poudel, Laure Menin, et al.. (2020). Biogeography of microbial bile acid transformations along the murine gut. Journal of Lipid Research. 61(11). 1450–1463. 65 indexed citations

13.

Shiradkar, Rakesh, Ananya Panda, Patrick Leo, et al.. (2020). Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology. 31(4). 2644–2644. 1 indexed citations

14.

Shiradkar, Rakesh, Ananya Panda, Patrick Leo, et al.. (2020). T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology. 31(3). 1336–1346. 32 indexed citations

15.

Nirschl, Jeffrey, Andrew Janowczyk, Eliot Peyster, et al.. (2018). A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS ONE. 13(4). e0192726–e0192726. 74 indexed citations

16.

Lu, Cheng, James S. Lewis, William D. Dupont, et al.. (2017). An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Modern Pathology. 30(12). 1655–1665. 65 indexed citations

17.

Romo‐Bucheli, David, Andrew Janowczyk, Hannah Gilmore, Eduardo Romero, & Anant Madabhushi. (2017). A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry Part A. 91(6). 566–573. 46 indexed citations

18.

Janowczyk, Andrew, Ajay Basavanhally, & Anant Madabhushi. (2016). Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics. 57. 50–61. 151 indexed citations

19.

Janowczyk, Andrew, Sharat Chandran, Rajesh R. Singh, et al.. (2011). High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts. IEEE Transactions on Biomedical Engineering. 59(5). 1240–1252. 26 indexed citations

20.

Xu, Jun, Andrew Janowczyk, Sharat Chandran, & Anant Madabhushi. (2011). A high-throughput active contour scheme for segmentation of histopathological imagery. Medical Image Analysis. 15(6). 851–862. 46 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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