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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...
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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...
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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...
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03/29/2023
Transductive few-shot adapters for medical image segmentation
With the recent raise of foundation models in computer vision and NLP, t...
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01/27/2023
Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows
In this paper, we propose an unsupervised framework based on normalizing...
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12/01/2022
Mutual Information-based Generalized Category Discovery
We introduce an information-maximization approach for the Generalized Ca...
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11/28/2022
Class Adaptive Network Calibration
Recent studies have revealed that, beyond conventional accuracy, calibra...
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11/25/2022
A Strong Baseline for Generalized Few-Shot Semantic Segmentation
This paper introduces a generalized few-shot segmentation framework with...
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06/18/2022
Attention-based Dynamic Subspace Learners for Medical Image Analysis
Learning similarity is a key aspect in medical image analysis, particula...
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03/10/2022
Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation
Semi-supervised segmentation tackles the scarcity of annotations by leve...
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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...
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03/03/2022
Constrained unsupervised anomaly segmentation
Current unsupervised anomaly localization approaches rely on generative ...
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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...
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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...
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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...
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09/21/2021
Mixed-supervised segmentation: Confidence maximization helps knowledge distillation
Despite achieving promising results in a breadth of medical image segmen...
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09/01/2021
Looking at the whole picture: constrained unsupervised anomaly segmentation
Current unsupervised anomaly localization approaches rely on generative ...
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08/06/2021
Source-Free Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interest for its capacity to adapt...
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06/23/2021
Mutual-Information Based Few-Shot Classification
We introduce Transductive Infomation Maximization (TIM) for few-shot lea...
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05/22/2021
Orthogonal Ensemble Networks for Biomedical Image Segmentation
Despite the astonishing performance of deep-learning based approaches fo...
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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...
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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...
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04/18/2021
The hidden label-marginal biases of segmentation losses
Most segmentation losses are arguably variants of the Cross-Entropy (CE)...
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04/06/2021
Weakly supervised segmentation with cross-modality equivariant constraints
Weakly supervised learning has emerged as an appealing alternative to al...
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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...
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12/15/2020
Teach me to segment with mixed supervision: Confident students become masters
Deep segmentation neural networks require large training datasets with p...
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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 ...
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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...
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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 ...
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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...
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08/25/2020
Transductive Information Maximization For Few-Shot Learning
We introduce Transductive Infomation Maximization (TIM) for few-shot lea...
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05/07/2020
Source-Relaxed Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interests for its capacity to adap...
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04/07/2020
Manifold-driven Attention Maps for Weakly Supervised Segmentation
Segmentation using deep learning has shown promising directions in medic...
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03/18/2020
Semi-supervised few-shot learning for medical image segmentation
Recent years have witnessed the great progress of deep neural networks o...
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03/09/2020
On the Texture Bias for Few-Shot CNN Segmentation
Despite the initial belief that Convolutional Neural Networks (CNNs) are...
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09/09/2019
Privacy-Net: An Adversarial Approach For Identity-obfuscated Segmentation
This paper presents a privacy-preserving network oriented towards medica...
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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...
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08/30/2019
Revisiting CycleGAN for semi-supervised segmentation
In this work, we study the problem of training deep networks for semanti...
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08/15/2019
Discretely-constrained deep network for weakly supervised segmentation
An efficient strategy for weakly-supervised segmentation is to impose co...
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08/08/2019
Constrained domain adaptation for segmentation
We propose to adapt segmentation networks with a constrained formulation...
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07/25/2019
Weakly Supervised Object Localization using Min-Max Entropy: an Interpretable Framework
Weakly supervised object localization (WSOL) models aim to locate object...
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06/07/2019
Multi-scale guided attention for medical image segmentation
Even though convolutional neural networks (CNNs) are driving progress in...
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04/10/2019
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised ...
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04/08/2019
Log-barrier constrained CNNs
This study investigates imposing inequality constraints on the outputs o...
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04/04/2019
On Direct Distribution Matching for Adapting Segmentation Networks
Minimization of distribution matching losses is a principled approach to...
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12/17/2018
Boundary loss for highly unbalanced segmentation
Widely used loss functions for convolutional neural network (CNN) segmen...
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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...
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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...
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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...
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07/09/2018
