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Found 32 papers, 15 papers with code
Separating common from salient patterns with Contrastive Representation Learning
Then, we motivate a novel Mutual Information minimization strategy to prevent information leakage between common and salient distributions.
Double InfoGAN for Contrastive Analysis
Experimental results on four visual datasets, from simple synthetic examples to complex medical images, show that the proposed method outperforms SOTA CA-VAEs in terms of latent separation and image quality.
Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR).
Decoupled conditional contrastive learning with variable metadata for prostate lesion detection
Early diagnosis of prostate cancer is crucial for efficient treatment.
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i. e., healthy subjects) and a target dataset (TG) (i. e., patients) from the ones that only exist in the target dataset.
Weakly-supervised positional contrastive learning: application to cirrhosis classification
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e. g., radiological scores).
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies
Identifying cirrhosis is key to correctly assess the health of the liver.
Contrastive learning for regression in multi-site brain age prediction
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers.
Unbiased Supervised Contrastive Learning
In this work, we tackle the problem of learning representations that are robust to biases.
Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs.
Anatomically constrained CT image translation for heterogeneous blood vessel segmentation
Anatomical structures such as blood vessels in contrast-enhanced CT (ceCT) images can be challenging to segment due to the variability in contrast medium diffusion.
Optimizing transformations for contrastive learning in a differentiable framework
Following previous works that introduce a small amount of supervision, we propose a framework to find optimal transformations for contrastive learning using a differentiable transformation network.
Is the U-Net Directional-Relationship Aware?
CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field.
Integrating Prior Knowledge in Contrastive Learning with Kernel
To this end, we use kernel theory to propose a novel loss, called decoupled uniformity, that i) allows the integration of prior knowledge and ii) removes the negative-positive coupling in the original InfoNCE loss.
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
In this paper we propose a novel framework based on deep learning to build a real-time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine.
Hair Color Digitization through Imaging and Deep Inverse Graphics
Since rendering realistic hair images requires path-tracing rendering, the conventional inverse graphics approach based on differentiable rendering is untractable.
A deep residual learning implementation of Metamorphosis
In medical imaging, most of the image registration methods implicitly assume a one-to-one correspondence between the source and target images (i. e., diffeomorphism).
Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels
However, a particularity of medical images is the availability of meta-data (such as age or sex) that can be exploited for learning representations.
Clinica: an open source software platform for reproducible clinical neuroscience studies
It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines.
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation
Due to a high heterogeneity in pose and size and to a limited number of available data, segmentation of pediatric images is challenging for deep learning methods.
Template-Based Graph Clustering
We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities).
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning
Our method is generic, it can integrate any clustering method and can be driven by both binary classification and regression.
Fast and Scalable Optimal Transport for Brain Tractograms
The parameters -- blur and reach -- of our method are meaningful, defining the minimum and maximum distance at which two fibers are compared with each other.
Knowledge distillation from multi-modal to mono-modal segmentation networks
In this paper, we propose KD-Net, a framework to transfer knowledge from a trained multi-modal network (teacher) to a mono-modal one (student).
Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution.
Metamorphic image registration using a semi-Lagrangian scheme
In this paper, we propose an implementation of both Large Deformation Diffeomorphic Metric Mapping (LDDMM) and Metamorphosis image registration using a semi-Lagrangian scheme for geodesic shooting.
Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning
Moreover, we showed that linear models perform comparably with SOTA CNN on VBM data.
Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example
While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task.
Approximation of dilation-based spatial relations to add structural constraints in neural networks
Several relations can be modeled as a morphological dilation of a reference object with a structuring element representing the semantics of the relation, from which the degree of satisfaction of the relation between another object and the reference object can be derived.
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer
While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications.
White Matter Fiber Segmentation Using Functional Varifolds
We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
Comparison of Distances for Supervised Segmentation of White Matter Tractography
For these reasons, in this work we compare many streamline distance functions available in the literature.
