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 semantic segmentation of natural and medical images: a review
This map shows the geographic impact of Joseph Cohen'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 Joseph Cohen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph Cohen more than expected).
This network shows the impact of papers produced by Joseph Cohen. 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 Joseph Cohen. The network helps show where Joseph Cohen may publish in the future.
Co-authorship network of co-authors of Joseph Cohen
This figure shows the co-authorship network connecting the top 25 collaborators of Joseph Cohen.
A scholar is included among the top collaborators of Joseph Cohen 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 Joseph Cohen. Joseph Cohen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Azad, Reza, Ehsan Khodapanah Aghdam, Amelie Rauland, et al.. (2024). Medical Image Segmentation Review: The Success of U-Net. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 10076–10095.231 indexed citations breakdown →
2.
Cohen, Joseph, Xun Huan, & Jun Ni. (2023). Fault Prognosis of Turbofan Engines. International Journal of Prognostics and Health Management. 14(2).3 indexed citations
Cohen, Joseph, Rupert Brooks, Evan J. Zucker, et al.. (2021). Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Progressive Exaggeration on Chest X-rays.. arXiv (Cornell University).2 indexed citations
6.
Cohen, Joseph, Rupert Brooks, Evan J. Zucker, et al.. (2021). Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays. 74–104.
7.
Cohen, Joseph, et al.. (2020). Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays. arXiv (Cornell University). 288–303.2 indexed citations
8.
Cohen, Joseph, et al.. (2020). On the limits of cross-domain generalization in automated X-ray prediction. 136–155.5 indexed citations
9.
Viviano, Joseph D., et al.. (2019). Underwhelming Generalization Improvements From Controlling Feature Attribution. arXiv (Cornell University).1 indexed citations
Cohen, Joseph, Henry Z. Lo, Tingting Lu, & Wei Ding. (2016). Crater Detection via Convolutional Neural Networks. arXiv (Cornell University). 1143.3 indexed citations
12.
Cohen, Joseph & Henry Z. Lo. (2014). Academic Torrents. 1–2.6 indexed citations
13.
Cohen, Joseph, et al.. (2012). Mars Weekend: A Panel and Games at the Museum of Science Boston. Lunar and Planetary Science Conference. 1023.1 indexed citations
14.
Cohen, Joseph. (2009). Alternances de la métaphysique : essais sur Emmanuel Levinas.1 indexed citations
15.
Cohen, Joseph, et al.. (2006). Heidegger : le danger et la promesse.1 indexed citations
16.
Cohen, Joseph & Jean‐Luc Nancy. (2005). Le spectre juif de Hegel.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.