José Dolz

3.9k total citations · 1 hit paper
69 papers, 1.7k citations indexed

About

José Dolz is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, José Dolz has authored 69 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Computer Vision and Pattern Recognition, 32 papers in Artificial Intelligence and 22 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in José Dolz's work include Advanced Neural Network Applications (24 papers), Medical Image Segmentation Techniques (19 papers) and Domain Adaptation and Few-Shot Learning (16 papers). José Dolz is often cited by papers focused on Advanced Neural Network Applications (24 papers), Medical Image Segmentation Techniques (19 papers) and Domain Adaptation and Few-Shot Learning (16 papers). José Dolz collaborates with scholars based in Canada, France and Spain. José Dolz's co-authors include Ismail Ben Ayed, Christian Desrosiers, Éric Granger, Hoel Kervadec, Hervé Lombaert, Laurent Massoptier, Yuri Boykov, Meng Tang, Maximilien Vermandel and Adrián Galdrán and has published in prestigious journals such as NeuroImage, Scientific Reports and The Journal of Urology.

In The Last Decade

José Dolz

61 papers receiving 1.7k citations

Hit Papers

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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.

Boundary loss for highly unbalanced segmentation

2020 274 citations Hoel Kervadec, Christian Desrosiers et al. Medical Image Analysis profile →

Peers

Countries citing papers authored by José Dolz

Since Specialization

Citations

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

Fields of papers citing papers by José Dolz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of José Dolz

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

#	Work	Indexed citations
1	Towards reliable WMH segmentation under domain shift: An application study using maximum entropy regularization to improve uncertainty estimation 2025 ·Computers in Biology and Medicine ·(unknown), Agostina J. Larrazabal, Diego H. Milone, José Dolz, Enzo Ferrante	1
2	GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation 2025 ·PolyPublie (École Polytechnique de Montréal) ·(unknown), José Dolz, Hervé Lombaert	0
3	Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection 2025 ·Espace ÉTS (ETS) ·(unknown), Gregory A. Lodygensky, Christian Desrosiers, José Dolz	0
4	Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI 2024 ·Cancers ·Kazım Gümüş, Julien Nicolas, Dheeraj Reddy Gopireddy, José Dolz, Seyed Behzad Jazayeri, Mark Bandyk	2
5	Anatomically-aware uncertainty for semi-supervised image segmentation 2023 ·Medical Image Analysis ·(unknown), José Dolz, Hervé Lombaert	26
6	Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey 2023 ·arXiv (Cornell University) ·Jérôme Rony, Soufiane Belharbi, José Dolz, Ismail Ben Ayed, Luke McCaffrey, Éric Granger	13
7	Calibrating segmentation networks with margin-based label smoothing 2023 ·Medical Image Analysis ·Balamurali Murugesan, (unknown), Adrián Galdrán, Ismail Ben Ayed, José Dolz	14
8	Do we really need dice? The hidden region-size biases of segmentation losses 2023 ·Medical Image Analysis ·(unknown), José Dolz, Adrián Galdrán, (unknown), Ismail Ben Ayed	14
9	Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation 2022 ·arXiv (Cornell University) ·(unknown), José Dolz, Hervé Lombaert	7
10	Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty 2021 ·IEEE Transactions on Medical Imaging ·Soufiane Belharbi, Jérôme Rony, José Dolz, Ismail Ben Ayed, Luke McCaffrey, Éric Granger	39
11	Joint progressive knowledge distillation and unsupervised domain adaptation 2020 ·Espace ÉTS (ETS) ·(unknown), Éric Granger, (unknown), José Dolz, (unknown)	14
12	Information Maximization for Few-Shot Learning 2020 ·Neural Information Processing Systems ·(unknown), (unknown), Jérôme Rony, José Dolz, Pablo Piantanida, Ismail Ben Ayed	32
13	Discretely-constrained deep network for weakly supervised segmentation 2020 ·Neural Networks ·Jizong Peng, Hoel Kervadec, José Dolz, Ismail Ben Ayed, Marco Pedersoli, Christian Desrosiers	23
14	Weakly Supervised Object Localization using Min-Max Entropy: an Interpretable Framework. 2019 ·arXiv (Cornell University) ·Soufiane Belharbi, Jérôme Rony, José Dolz, Ismail Ben Ayed, Luke McCaffrey, Éric Granger	1
15	HyperDense-Net: A hyper-densely connected CNN for multi-modal image semantic segmentation. 2017 ·arXiv (Cornell University) ·José Dolz, Ismail Ben Ayed, Jing Yuan, Christian Desrosiers	5
16	3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study 2017 ·NeuroImage ·José Dolz, Christian Desrosiers, Ismail Ben Ayed	284
17	Fully Automatic Danger Zone Determination for SBRT in NSCLC 2015 ·Journal of Radiation Oncology ·Tobias Fechter, José Dolz, Alin Chirindel, (unknown), M. Carles, Sonja Adebahr, Michael Mix, Ursula Nestle	1
18	User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy 2015 ·Journal of Digital Imaging ·(unknown), José Dolz, Hortense A. Kirişli, Sonja Adebahr, Tanja Schimek‐Jasch, Ursula Nestle, Laurent Massoptier, Edit Varga, Pieter Jan Stappers, Wiro J. Niessen, Yu Song	26
19	Segmentation algorithms of subcortical brain structures on MRI:a review 2014 ·LillOA (Université de Lille (University Of Lille)) ·José Dolz, Laurent Massoptier, Maximilien Vermandel	1
20	Industrial indoor environment characterization - Propagation models 2011 ·International Symposium on Electromagnetic Compatibility ·(unknown), José Dolz, Peter Stenumgaard, (unknown), José Chilo	12

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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