Search Results for author: Jose Dolz

Found 52 papers, 38 papers with code

The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration

1 code implementation30 Nov 2021 Bingyuan Liu, Ismail Ben Ayed, Adrian Galdran, Jose Dolz

Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.

Image Classification Semantic Segmentation

Mixed-supervised segmentation: Confidence maximization helps knowledge distillation

1 code implementation21 Sep 2021 Bingyuan Liu, Christian Desrosiers, Ismail Ben Ayed, Jose Dolz

In this work, we propose a dual-branch architecture, where the upper branch (teacher) receives strong annotations, while the bottom one (student) is driven by limited supervision and guided by the upper branch.

Knowledge Distillation Medical Image Segmentation

Looking at the whole picture: constrained unsupervised anomaly segmentation

1 code implementation1 Sep 2021 Julio Silva-Rodríguez, Valery Naranjo, Jose Dolz

In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.

Lesion Segmentation

Source-Free Domain Adaptation for Image Segmentation

1 code implementation6 Aug 2021 Mathilde Bateson, Jose Dolz, Hoel Kervadec, Hervé Lombaert, Ismail Ben Ayed

Our method yields comparable results to several state of the art adaptation techniques, despite having access to much less information, as the source images are entirely absent in our adaptation phase.

Cardiac Segmentation Domain Adaptation +1

Mutual-Information Based Few-Shot Classification

2 code implementations23 Jun 2021 Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Pablo Piantanida

We motivate our transductive loss by deriving a formal relation between the classification accuracy and mutual-information maximization.

Few-Shot Learning

Transductive Few-Shot Learning: Clustering is All You Need?

1 code implementation16 Jun 2021 Imtiaz Masud Ziko, Malik Boudiaf, Jose Dolz, Eric Granger, Ismail Ben Ayed

Surprisingly, we found that even standard clustering procedures (e. g., K-means), which correspond to particular, non-regularized cases of our general model, already achieve competitive performances in comparison to the state-of-the-art in few-shot learning.

Few-Shot Learning

Orthogonal Ensemble Networks for Biomedical Image Segmentation

1 code implementation22 May 2021 Agostina J. Larrazabal, César Martínez, Jose Dolz, Enzo Ferrante

Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which could be harmful for critical decision-making processes.

Decision Making Ensemble Learning +2

Self-learning for weakly supervised Gleason grading of local patterns

1 code implementation21 May 2021 Julio Silva-Rodríguez, Adrián Colomer, Jose Dolz, Valery Naranjo

Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (k) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task.

whole slide images

Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!

1 code implementation3 May 2021 Hoel Kervadec, Houda Bahig, Laurent Letourneau-Guillon, Jose Dolz, Ismail Ben Ayed

We also found that shape descriptors can be a valid way to encode anatomical priors about the task, enabling to leverage expert knowledge without additional annotations.

Medical Image Segmentation

The hidden label-marginal biases of segmentation losses

1 code implementation18 Apr 2021 Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed

In the abundant segmentation literature, there is no clear consensus as to which of these losses is a better choice, with varying performances for each across different benchmarks and applications.

Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer

1 code implementation13 Apr 2021 Le Thanh Nguyen-Meidine, Madhu Kiran, Marco Pedersoli, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger

Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions.

Incremental Learning Knowledge Distillation +4

Weakly supervised segmentation with cross-modality equivariant constraints

1 code implementation6 Apr 2021 Gaurav Patel, Jose Dolz

In addition, we add a KL-divergence on the class prediction distributions to facilitate the information exchange between modalities, which, combined with the equivariant regularizers further improves the performance of our model.

Data Augmentation Semantic Segmentation +1

Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains

no code implementations18 Jan 2021 Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin, Eric Granger

Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets.

Knowledge Distillation Person Re-Identification +1

Bladder segmentation based on deep learning approaches: current limitations and lessons

no code implementations16 Jan 2021 Mark G. Bandyk, Dheeraj R Gopireddy, Chandana Lall, K. C. Balaji, Jose Dolz

Nevertheless, despite the success of these models in other medical problems, progress in multi region bladder segmentation is still at a nascent stage, with just a handful of works tackling a multi region scenario.

Bladder Segmentation

Teach me to segment with mixed supervision: Confident students become masters

1 code implementation15 Dec 2020 Jose Dolz, Christian Desrosiers, Ismail Ben Ayed

In conjunction with a standard cross-entropy over the labeled pixels, our novel formulation integrates two important terms: (i) a Shannon entropy loss defined over the less-supervised images, which encourages confident student predictions at the bottom branch; and (ii) a Kullback-Leibler (KL) divergence, which transfers the knowledge from the predictions generated by the strongly supervised branch to the less-supervised branch, and guides the entropy (student-confidence) term to avoid trivial solutions.

Semantic Segmentation

Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

2 code implementations CVPR 2021 Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud, Pablo Piantanida, Ismail Ben Ayed, Jose Dolz

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm.

Few-Shot Semantic Segmentation

Privacy Preserving for Medical Image Analysis via Non-Linear Deformation Proxy

no code implementations25 Nov 2020 Bach Ngoc Kim, Jose Dolz, Christian Desrosiers, Pierre-Marc Jodoin

Results show that the segmentation accuracy of our method is similar to a system trained on non-encoded images, while considerably reducing the ability to recover subject identity.

Brain Segmentation

Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

2 code implementations14 Nov 2020 Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations.

General Classification Weakly supervised segmentation

Laplacian Regularized Few-Shot Learning

1 code implementation28 Jun 2020 Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed

Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set.

Few-Shot Learning Graph Clustering

Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation

2 code implementations16 May 2020 Le Thanh Nguyen-Meidine, Eric Granger, Madhu Kiran, Jose Dolz, Louis-Antoine Blais-Morin

In both datasets, results indicate that our method can achieve the highest level of accuracy while requiring a comparable or lower time complexity.

Knowledge Distillation Person Re-Identification +1

Source-Relaxed Domain Adaptation for Image Segmentation

3 code implementations7 May 2020 Mathilde Bateson, Hoel Kervadec, Jose Dolz, Herve Lombaert, Ismail Ben Ayed

Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with a domain invariant prior on the segmentation regions.

Domain Adaptation Semantic Segmentation

Manifold-driven Attention Maps for Weakly Supervised Segmentation

no code implementations7 Apr 2020 Sukesh Adiga V, Jose Dolz, Herve Lombaert

Segmentation using deep learning has shown promising directions in medical imaging as it aids in the analysis and diagnosis of diseases.

General Classification Semantic Segmentation +1

Semi-supervised few-shot learning for medical image segmentation

no code implementations18 Mar 2020 Abdur R Feyjie, Reza Azad, Marco Pedersoli, Claude Kauffman, Ismail Ben Ayed, Jose Dolz

To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning.

Few-Shot Learning Lesion Segmentation

On the Texture Bias for Few-Shot CNN Segmentation

1 code implementation9 Mar 2020 Reza Azad, Abdur R Fayjie, Claude Kauffman, Ismail Ben Ayed, Marco Pedersoli, Jose Dolz

Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs provides higher performing models when learning on large labeled training datasets.

Few-Shot Semantic Segmentation Semantic Segmentation

Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images

no code implementations9 Sep 2019 Bach Ngoc Kim, Jose Dolz, Pierre-Marc Jodoin, Christian Desrosiers

Our novel architecture is composed of three components: 1) an encoder network which removes identity-specific features from input medical images, 2) a discriminator network that attempts to identify the subject from the encoded images, 3) a medical image analysis network which analyzes the content of the encoded images (segmentation in our case).

Deep weakly-supervised learning methods for classification and localization in histology images: a survey

1 code implementation8 Sep 2019 Jérôme Rony, Soufiane Belharbi, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

In this survey, deep weakly-supervised learning (WSL) models are investigated to identify and locate diseases in histology images, without the need for pixel-level annotations.

General Classification

Revisiting CycleGAN for semi-supervised segmentation

1 code implementation30 Aug 2019 Arnab Kumar Mondal, Aniket Agarwal, Jose Dolz, Christian Desrosiers

In this work, we study the problem of training deep networks for semantic image segmentation using only a fraction of annotated images, which may significantly reduce human annotation efforts.

Semantic Segmentation Style Transfer

Constrained domain adaptation for Image segmentation

1 code implementation8 Aug 2019 Mathilde Bateson, Jose Dolz, Hoel Kervadec, Hervé Lombaert, Ismail Ben Ayed

We propose to adapt segmentation networks with a constrained formulation, which embeds domain-invariant prior knowledge about the segmentation regions.

Domain Adaptation Semantic Segmentation

Min-max Entropy for Weakly Supervised Pointwise Localization

1 code implementation25 Jul 2019 Soufiane Belharbi, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger

Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.

Weakly-Supervised Object Localization

Multi-scale self-guided attention for medical image segmentation

1 code implementation arXiv preprint 2019 Ashish Sinha, Jose Dolz

In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms.

Attentive segmentation networks Brain Tumor Segmentation +1

Curriculum semi-supervised segmentation

1 code implementation10 Apr 2019 Hoel Kervadec, Jose Dolz, Eric Granger, Ismail Ben Ayed

This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region.

Left Ventricle Segmentation Semi-Supervised Semantic Segmentation

Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions

1 code implementation8 Apr 2019 Hoel Kervadec, Jose Dolz, Jing Yuan, Christian Desrosiers, Eric Granger, Ismail Ben Ayed

While sub-optimality is not guaranteed for non-convex problems, our result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables.

Stochastic Optimization Weakly supervised segmentation

IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet

1 code implementation19 Nov 2018 Jose Dolz, Christian Desrosiers, Ismail Ben Ayed

Despite the technological advances in medical imaging, IVD localization and segmentation are still manually performed, which is time-consuming and prone to errors.

Medical Image Segmentation

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

1 code implementation29 Oct 2018 Arnab Kumar Mondal, Jose Dolz, Christian Desrosiers

In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.

3D Medical Imaging Segmentation Brain Image Segmentation +3

Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities

no code implementations16 Oct 2018 Jose Dolz, Ismail Ben Ayed, Christian Desrosiers

First, instead of combining the available image modalities at the input, each of them is processed in a different path to better exploit their unique information.

Ischemic Stroke Lesion Segmentation Lesion Segmentation

An Attention Model for group-level emotion recognition

1 code implementation9 Jul 2018 Aarush Gupta, Dakshit Agrawal, Hardik Chauhan, Jose Dolz, Marco Pedersoli

In this paper we propose a new approach for classifying the global emotion of images containing groups of people.

Emotion Recognition

Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

no code implementations28 May 2018 Jose Dolz, Xiaopan Xu, Jerome Rony, Jing Yuan, Yang Liu, Eric Granger, Christian Desrosiers, Xi Zhang, Ismail Ben Ayed, Hongbing Lu

Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC).

Constrained-CNN losses for weakly supervised segmentation

4 code implementations12 May 2018 Hoel Kervadec, Jose Dolz, Meng Tang, Eric Granger, Yuri Boykov, Ismail Ben Ayed

To the best of our knowledge, the method of [Pathak et al., 2015] is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation.

Medical Image Segmentation Weakly supervised segmentation +1

HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

3 code implementations9 Apr 2018 Jose Dolz, Karthik Gopinath, Jing Yuan, Herve Lombaert, Christian Desrosiers, Ismail Ben Ayed

Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.

Brain Segmentation Image Classification +2

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

1 code implementation14 Dec 2017 Jose Dolz, Christian Desrosiers, Li Wang, Jing Yuan, Dinggang Shen, Ismail Ben Ayed

We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

Infant Brain Mri Segmentation MRI segmentation

Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network

1 code implementation16 Oct 2017 Jose Dolz, Ismail Ben Ayed, Jing Yuan, Christian Desrosiers

Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions.

Brain Segmentation Infant Brain Mri Segmentation +1

Unbiased Shape Compactness for Segmentation

1 code implementation28 Apr 2017 Jose Dolz, Ismail Ben Ayed, Christian Desrosiers

We propose to constrain segmentation functionals with a dimensionless, unbiased and position-independent shape compactness prior, which we solve efficiently with an alternating direction method of multipliers (ADMM).

A 3D fully convolutional neural network and a random walker to segment the esophagus in CT

no code implementations21 Apr 2017 Tobias Fechter, Sonja Adebahr, Dimos Baltas, Ismail Ben Ayed, Christian Desrosiers, Jose Dolz

These figures translate into a very good agreement with the reference contours and an increase in accuracy compared to other methods.

DOPE: Distributed Optimization for Pairwise Energies

no code implementations CVPR 2017 Jose Dolz, Ismail Ben Ayed, Christian Desrosiers

We formulate an Alternating Direction Method of Mul-tipliers (ADMM) that systematically distributes the computations of any technique for optimizing pairwise functions, including non-submodular potentials.

Distributed Optimization

A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients

no code implementations30 Mar 2017 Jose Dolz, Nicolas Reyns, Nacim Betrouni, Dris Kharroubi, Mathilde Quidet, Laurent Massoptier, Maximilien Vermandel

Prescribed radiation therapy for brain cancer requires precisely defining the target treatment area, as well as delineating vital brain structures which must be spared from radiotoxicity.

General Classification

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