Mane Williams

1.6k total citations · 2 hit papers
6 papers, 707 citations indexed

About

Mane Williams is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Surgery. According to data from OpenAlex, Mane Williams has authored 6 papers receiving a total of 707 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Surgery. Recurrent topics in Mane Williams's work include AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Cancer Genomics and Diagnostics (2 papers). Mane Williams is often cited by papers focused on AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Cancer Genomics and Diagnostics (2 papers). Mane Williams collaborates with scholars based in United States, Switzerland and Canada. Mane Williams's co-authors include Faisal Mahmood, Ming Y. Lu, Richard J. Chen, Drew F. K. Williamson, Muhammad Shaban, Tiffany Chen, Jana Lipková, Maha Shady, Bowen Chen and Andrew Zhang and has published in prestigious journals such as Cell, Nature Medicine and Journal of the American College of Cardiology.

In The Last Decade

Mane Williams

6 papers receiving 698 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.

Towards a general-purpose foundation model for computational pathology

2024 331 citations Richard J. Chen, Tong Ding et al. Nature Medicine profile →
Pan-cancer integrative histology-genomic analysis via multimodal deep learning

2022 291 citations Richard J. Chen, Ming Y. Lu et al. Cancer Cell profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mane Williams United States	5	431	338	127	114	110	6	707
Pooya Mobadersany United States	5	432 1.0×	420 1.2×	114 0.9×	125 1.1×	94 0.9×	9	716
Anurag Vaidya United States	8	480 1.1×	407 1.2×	130 1.0×	107 0.9×	117 1.1×	10	865
Chengkuan Chen United States	4	488 1.1×	372 1.1×	113 0.9×	92 0.8×	177 1.6×	5	807
Benoît Schmauch France	8	370 0.9×	448 1.3×	111 0.9×	134 1.2×	69 0.6×	14	763
Guillaume Jaume United States	11	577 1.3×	401 1.2×	124 1.0×	85 0.7×	210 1.9×	14	885
Amelie Echle Germany	9	362 0.8×	350 1.0×	84 0.7×	115 1.0×	67 0.6×	10	618
Maha Shady United States	5	354 0.8×	270 0.8×	97 0.8×	98 0.9×	91 0.8×	10	522
Andrew Zhang United States	4	362 0.8×	265 0.8×	88 0.7×	58 0.5×	120 1.1×	6	619
Charlie Saillard France	6	379 0.9×	433 1.3×	116 0.9×	145 1.3×	51 0.5×	12	754
José E. Velázquez Vega United States	7	403 0.9×	414 1.2×	153 1.2×	139 1.2×	77 0.7×	15	821

Countries citing papers authored by Mane Williams

Since Specialization

Citations

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

Fields of papers citing papers by Mane Williams

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mane Williams

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

6 of 6 papers shown

1.

Song, Andrew H., Mane Williams, Drew F. K. Williamson, et al.. (2024). Analysis of 3D pathology samples using weakly supervised AI. Cell. 187(10). 2502–2520.e17. 27 indexed citations

2.

Chen, Richard J., Tong Ding, Ming Y. Lu, et al.. (2024). Towards a general-purpose foundation model for computational pathology. Nature Medicine. 30(3). 850–862. 331 indexed citations breakdown →

3.

Lipková, Jana, Tiffany Chen, Ming Y. Lu, et al.. (2022). Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies. Nature Medicine. 28(3). 575–582. 52 indexed citations

4.

Chen, Richard J., Ming Y. Lu, Drew F. K. Williamson, et al.. (2022). Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell. 40(8). 865–878.e6. 291 indexed citations breakdown →

5.

Chen, Richard J., Ming Y. Lu, Drew F. K. Williamson, et al.. (2022). PAN-CANCER INTEGRATIVE HISTOLOGY-GENOMIC ANALYSIS VIA INTERPRETABLE MULTIMODAL DEEP LEARNING. Journal of Pathology Informatics. 13. 100057–100057. 5 indexed citations

6.

Lipková, Jana, Tiffany Chen, Ming Y. Lu, et al.. (2021). INTERNATIONAL EVALUATION OF WEAKLY-SUPERVISED AI-MODEL FOR CARDIAC ALLOGRAFT REJECTION SCREENING. Journal of the American College of Cardiology. 77(18). 520–520. 1 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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