b'Jose Dolz'

research

∙ 08/15/2023

A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision

Foundation vision-language models are currently transforming computer vi...

0 Julio Silva-Rodríguez, et al. ∙

research

∙ 07/11/2023

MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning

Despite the recent progress in incremental learning, addressing catastro...

0 Julien Nicolas, et al. ∙

research

∙ 05/23/2023

Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation

Privacy protection in medical data is a legitimate obstacle for centrali...

0 Bach Kim, et al. ∙

research

∙ 03/29/2023

Transductive few-shot adapters for medical image segmentation

With the recent raise of foundation models in computer vision and NLP, t...

0 Julio Silva-Rodríguez, et al. ∙

research

∙ 01/27/2023

Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows

In this paper, we propose an unsupervised framework based on normalizing...

0 Farzad Beizaee, et al. ∙

research

∙ 12/01/2022

Mutual Information-based Generalized Category Discovery

We introduce an information-maximization approach for the Generalized Ca...

0 Florent Chiaroni, et al. ∙

research

∙ 11/28/2022

Class Adaptive Network Calibration

Recent studies have revealed that, beyond conventional accuracy, calibra...

0 Bingyuan Liu, et al. ∙

research

∙ 11/25/2022

A Strong Baseline for Generalized Few-Shot Semantic Segmentation

This paper introduces a generalized few-shot segmentation framework with...

0 Sina Hajimiri, et al. ∙

research

∙ 06/18/2022

Attention-based Dynamic Subspace Learners for Medical Image Analysis

Learning similarity is a key aspect in medical image analysis, particula...

0 Sukesh Adiga V, et al. ∙

research

∙ 03/10/2022

Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation

Semi-supervised segmentation tackles the scarcity of annotations by leve...

0 Sukesh Adiga V, et al. ∙

research

∙ 03/07/2022

On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation

Semi-supervised learning has emerged as an appealing strategy to train d...

0 Martin Van Waerebeke, et al. ∙

research

∙ 03/03/2022

Constrained unsupervised anomaly segmentation

Current unsupervised anomaly localization approaches rely on generative ...

0 Julio Silva-Rodríguez, et al. ∙

research

∙ 12/22/2021

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation

Modern deep neural networks have achieved remarkable progress in medical...

8 Agostina Larrazabal, et al. ∙

research

∙ 11/30/2021

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

In spite of the dominant performances of deep neural networks, recent wo...

0 Bingyuan Liu, et al. ∙

research

∙ 11/23/2021

A self-training framework for glaucoma grading in OCT B-scans

In this paper, we present a self-training-based framework for glaucoma g...

0 Gabriel Garcia, et al. ∙

research

∙ 09/21/2021

Mixed-supervised segmentation: Confidence maximization helps knowledge distillation

Despite achieving promising results in a breadth of medical image segmen...

8 Bingyuan Liu, et al. ∙

research

∙ 09/01/2021

Looking at the whole picture: constrained unsupervised anomaly segmentation

Current unsupervised anomaly localization approaches rely on generative ...

1 Julio Silva-Rodríguez, et al. ∙

research

∙ 08/06/2021

Source-Free Domain Adaptation for Image Segmentation

Domain adaptation (DA) has drawn high interest for its capacity to adapt...

16 Mathilde Bateson, et al. ∙

research

∙ 06/23/2021

Mutual-Information Based Few-Shot Classification

We introduce Transductive Infomation Maximization (TIM) for few-shot lea...

0 Malik Boudiaf, et al. ∙

research

∙ 05/22/2021

Orthogonal Ensemble Networks for Biomedical Image Segmentation

Despite the astonishing performance of deep-learning based approaches fo...

37 Agostina J. Larrazabal, et al. ∙

research

∙ 05/21/2021

Self-learning for weakly supervised Gleason grading of local patterns

Prostate cancer is one of the main diseases affecting men worldwide. The...

0 Julio Silva-Rodríguez, et al. ∙

research

∙ 05/03/2021

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

Standard losses for training deep segmentation networks could be seen as...

0 Hoel Kervadec, et al. ∙

research

∙ 04/18/2021

The hidden label-marginal biases of segmentation losses

Most segmentation losses are arguably variants of the Cross-Entropy (CE)...

0 Bingyuan Liu, et al. ∙

research

∙ 04/06/2021

Weakly supervised segmentation with cross-modality equivariant constraints

Weakly supervised learning has emerged as an appealing alternative to al...

3 Gaurav patel, et al. ∙

research

∙ 01/16/2021

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

Precise determination and assessment of bladder cancer (BC) extent of mu...

23 Mark G. Bandyk, et al. ∙

research

∙ 12/15/2020

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

Deep segmentation neural networks require large training datasets with p...

0 Jose Dolz, et al. ∙

research

∙ 12/11/2020

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

Few-shot segmentation has recently attracted substantial interest, with ...

0 Malik Boudiaf, et al. ∙

research

∙ 11/25/2020

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

We propose a client-server system which allows for the analysis of multi...

0 Bach Ngoc Kim, et al. ∙

research

∙ 10/01/2020

Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images

Assessing the degree of disease severity in biomedical images is a task ...

13 Adrian Galdran, et al. ∙

research

∙ 09/03/2020

The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models

The segmentation of the retinal vasculature from eye fundus images repre...

13 Adrian Galdran, et al. ∙

research

∙ 08/25/2020

Transductive Information Maximization For Few-Shot Learning

We introduce Transductive Infomation Maximization (TIM) for few-shot lea...

18 Malik Boudiaf, et al. ∙

research

∙ 05/07/2020

Source-Relaxed Domain Adaptation for Image Segmentation

Domain adaptation (DA) has drawn high interests for its capacity to adap...

7 Mathilde Bateson, et al. ∙

research

∙ 04/07/2020

Manifold-driven Attention Maps for Weakly Supervised Segmentation

Segmentation using deep learning has shown promising directions in medic...

69 Sukesh Adiga V, et al. ∙

research

∙ 03/18/2020

Semi-supervised few-shot learning for medical image segmentation

Recent years have witnessed the great progress of deep neural networks o...

51 Abdur R Feyjie, et al. ∙

research

∙ 03/09/2020

On the Texture Bias for Few-Shot CNN Segmentation

Despite the initial belief that Convolutional Neural Networks (CNNs) are...

9 Reza Azad, et al. ∙

research

∙ 09/09/2019

Privacy-Net: An Adversarial Approach For Identity-obfuscated Segmentation

This paper presents a privacy-preserving network oriented towards medica...

12 Bach Ngoc Kim, et al. ∙

research

∙ 09/08/2019

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

Using state-of-the-art deep learning models for the computer-assisted di...

8 Jerome Rony, et al. ∙

research

∙ 08/30/2019

Revisiting CycleGAN for semi-supervised segmentation

In this work, we study the problem of training deep networks for semanti...

12 Arnab Kumar Mondal, et al. ∙

research

∙ 08/15/2019

Discretely-constrained deep network for weakly supervised segmentation

An efficient strategy for weakly-supervised segmentation is to impose co...

8 Jizong Peng, et al. ∙

research

∙ 08/08/2019

Constrained domain adaptation for segmentation

We propose to adapt segmentation networks with a constrained formulation...

3 Mathilde Bateson, et al. ∙

research

∙ 07/25/2019

Weakly Supervised Object Localization using Min-Max Entropy: an Interpretable Framework

Weakly supervised object localization (WSOL) models aim to locate object...

4 Soufiane Belharbi, et al. ∙

research

∙ 06/07/2019

Multi-scale guided attention for medical image segmentation

Even though convolutional neural networks (CNNs) are driving progress in...

10 Ashish Sinha, et al. ∙

research

∙ 04/10/2019

Curriculum semi-supervised segmentation

This study investigates a curriculum-style strategy for semi-supervised ...

0 Hoel Kervadec, et al. ∙

research

∙ 04/08/2019

Log-barrier constrained CNNs

This study investigates imposing inequality constraints on the outputs o...

0 Hoel Kervadec, et al. ∙

research

∙ 04/04/2019

On Direct Distribution Matching for Adapting Segmentation Networks

Minimization of distribution matching losses is a principled approach to...

0 Georg Pichler, et al. ∙

research

∙ 12/17/2018

Boundary loss for highly unbalanced segmentation

Widely used loss functions for convolutional neural network (CNN) segmen...

0 Hoel Kervadec, et al. ∙

research

∙ 11/19/2018

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

Accurate localization and segmentation of intervertebral disc (IVD) is c...

0 Jose Dolz, et al. ∙

research

∙ 10/29/2018

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

We address the problem of segmenting 3D multi-modal medical images in sc...

3 Arnab Kumar Mondal, et al. ∙

research

∙ 10/16/2018

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

Delineating infarcted tissue in ischemic stroke lesions is crucial to de...

0 Jose Dolz, et al. ∙

research

∙ 07/09/2018

An Attention Model for group-level emotion recognition

In this paper we propose a new approach for classifying the global emoti...

0 Aarush Gupta, et al. ∙

Jose Dolz

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