Sailesh Conjeti

3.5k total citations · 2 hit papers
38 papers, 1.8k citations indexed

About

Sailesh Conjeti is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Sailesh Conjeti has authored 38 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 8 papers in Biomedical Engineering. Recurrent topics in Sailesh Conjeti's work include Medical Image Segmentation Techniques (8 papers), Image Retrieval and Classification Techniques (6 papers) and AI in cancer detection (5 papers). Sailesh Conjeti is often cited by papers focused on Medical Image Segmentation Techniques (8 papers), Image Retrieval and Classification Techniques (6 papers) and AI in cancer detection (5 papers). Sailesh Conjeti collaborates with scholars based in Germany, India and United States. Sailesh Conjeti's co-authors include Nassir Navab, Abhijit Guha Roy, Christian Wachinger, Debdoot Sheet, Amin Katouzian, Rahul Banerjee, Rajiv Ranjan Singh, Sri Phani Krishna Karri, Santiago Estrada and Martin Reuter and has published in prestigious journals such as NeuroImage, IEEE Access and Medical Image Analysis.

In The Last Decade

Sailesh Conjeti

38 papers receiving 1.7k 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.

ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks

2017 445 citations Abhijit Guha Roy, Sailesh Conjeti et al. Biomedical Optics Express profile →
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline

2020 300 citations Leonie Henschel, Sailesh Conjeti et al. NeuroImage profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sailesh Conjeti Germany	15	899	519	426	324	298	38	1.8k
Anjan Gudigar India	27	952 1.1×	633 1.2×	243 0.6×	457 1.4×	320 1.1×	58	2.0k
Christian Wachinger Germany	20	1.2k 1.4×	991 1.9×	528 1.2×	437 1.3×	300 1.0×	76	2.6k
Georgy Gimel’farb New Zealand	25	1.5k 1.6×	1.0k 2.0×	360 0.8×	494 1.5×	147 0.5×	196	2.8k
George K. Matsopoulos Greece	23	689 0.8×	590 1.1×	293 0.7×	299 0.9×	77 0.3×	195	2.3k
Kai Ma China	27	764 0.8×	730 1.4×	317 0.7×	717 2.2×	88 0.3×	118	2.5k
Joel E.W. Koh Singapore	29	877 1.0×	437 0.8×	368 0.9×	399 1.2×	471 1.6×	61	2.9k
Jinzhu Yang China	23	621 0.7×	434 0.8×	121 0.3×	426 1.3×	64 0.2×	147	1.6k
Qiang Chen China	24	1.1k 1.2×	462 0.9×	382 0.9×	168 0.5×	745 2.5×	110	1.8k
Dwarikanath Mahapatra Switzerland	23	905 1.0×	1.1k 2.0×	207 0.5×	464 1.4×	352 1.2×	87	1.8k
Qingmao Hu China	26	853 0.9×	1.0k 2.0×	303 0.7×	377 1.2×	37 0.1×	135	2.6k

Countries citing papers authored by Sailesh Conjeti

Since Specialization

Citations

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

Fields of papers citing papers by Sailesh Conjeti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sailesh Conjeti

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

Conjeti, Sailesh, et al.. (2020). Detection of Breast Cancer From Whole Slide Histopathological Images Using Deep Multiple Instance CNN. IEEE Access. 8. 213502–213511. 36 indexed citations

Estrada, Santiago, et al.. (2020). FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI.. DZNE Pub. 59 indexed citations

Henschel, Leonie, Sailesh Conjeti, Santiago Estrada, et al.. (2020). FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. NeuroImage. 219. 117012–117012. 300 indexed citations breakdown →

Roy, Abhijit Guha, Sailesh Conjeti, Nassir Navab, & Christian Wachinger. (2019). Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control. NeuroImage. 195. 11–22. 76 indexed citations

Roy, Abhijit Guha, Sailesh Conjeti, Nassir Navab, & Christian Wachinger. (2018). QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds.. arXiv (Cornell University). 8 indexed citations

Roy, Abhijit Guha, Sailesh Conjeti, Nassir Navab, & Christian Wachinger. (2018). QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. NeuroImage. 186. 713–727. 145 indexed citations

Roy, Abhijit Guha, Sailesh Conjeti, Sri Phani Krishna Karri, et al.. (2017). ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomedical Optics Express. 8(8). 3627–3627. 445 indexed citations breakdown →

Kazi, Anees, Sailesh Conjeti, Amin Katouzian, & Nassir Navab. (2017). Coupled Manifold Learning for Retrieval Across Modalities. 290. 1321–1328. 1 indexed citations

Pölsterl, Sebastian, Sailesh Conjeti, Nassir Navab, & Amin Katouzian. (2016). Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection. Artificial Intelligence in Medicine. 72. 1–11. 37 indexed citations

10.

Conjeti, Sailesh, Amin Katouzian, Abhijit Guha Roy, et al.. (2016). Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization. Medical Image Analysis. 32. 1–17. 12 indexed citations

11.

Conjeti, Sailesh, Amin Katouzian, Anees Kazi, et al.. (2016). Metric hashing forests. Medical Image Analysis. 34. 13–29. 12 indexed citations

12.

Conjeti, Sailesh, et al.. (2016). Neuron-Miner: An Advanced Tool for Morphological Search and Retrieval in Neuroscientific Image Databases. Neuroinformatics. 14(4). 369–385. 12 indexed citations

13.

Roy, Abhijit Guha, Sailesh Conjeti, Stéphane Carlier, et al.. (2016). Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography. 1359–1362. 8 indexed citations

14.

Albarqouni, Shadi, et al.. (2015). Multi-scale Graph-based Guided Filter for De-noising Cryo-Electron Tomographic Data. 17.1–17.10. 1 indexed citations

15.

Conjeti, Sailesh, Stéphane Carlier, Andreas König, et al.. (2015). Bag of forests for modelling of tissue energy interaction in optical coherence tomography for atherosclerotic plaque susceptibility assessment. 13. 428–431. 3 indexed citations

16.

Conjeti, Sailesh, Raunak Kumar Das, Mousumi Pal, et al.. (2014). Computer‐aided molecular pathology interpretation in exploring prospective markers for oral submucous fibrosis progression. Head & Neck. 38(5). 653–669. 14 indexed citations

17.

Conjeti, Sailesh, et al.. (2014). Full-Wave Intravascular Ultrasound Simulation from Histology. Lecture notes in computer science. 17(Pt 2). 627–634. 3 indexed citations

18.

Peng, Tingying, Lichao Wang, Christine Bayer, et al.. (2014). Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition. Lecture notes in computer science. 17(Pt 1). 33–40. 9 indexed citations

19.

Bag, Swarnendu, Sailesh Conjeti, Raunak Kumar Das, et al.. (2013). Computational analysis of p63+ nuclei distribution pattern by graph theoretic approach in an oral pre-cancer (sub-mucous fibrosis). Journal of Pathology Informatics. 4(1). 35–35. 14 indexed citations

20.

Singh, Rajiv Ranjan, Sailesh Conjeti, & Rahul Banerjee. (2013). Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios. International Journal of Computational Intelligence Systems. 7(5). 909–909. 23 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