10 citations · 20 across the 12 of their papers we have counts for
13 papers
Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
Rebeca Vétil, Clément Abi-Nader, Alexandre Bône +4
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR). To do so, we extract DLR features using a VAE while enfor…
Decoupled conditional contrastive learning with variable metadata for prostate lesion detection
Camille Ruppli, Pietro Gori, Roberto Ardon +1
Early diagnosis of prostate cancer is crucial for efficient treatment. Multi-parametric Magnetic Resonance Images (mp-MRI) are widely used for lesion detection. The Prostate Imagin…
Weakly-supervised positional contrastive learning: application to cirrhosis classification
Emma Sarfati, Alexandre Bône, Marc-Michel Rohé +2
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e.g., radiological scores). Access to high-confidence labels, such as histolog…
Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation
Hugo Oliveira, Pedro H. T. Gama, Isabelle Bloch +1
Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of works in other tasks {such} as segmentation and detection.…
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies
Emma Sarfati, Alexandre Bone, Marc-Michel Rohe +2
Identifying cirrhosis is key to correctly assess the health of the liver. However, the gold standard diagnosis of the cirrhosis needs a medical intervention to obtain the histologi…
Model-based inexact graph matching on top of CNNs for semantic scene understanding
Jérémy Chopin, Jean-Baptiste Fasquel, Harold Mouchère +2
Deep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing modu…
Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
Rebeca Vétil, Clément Abi Nader, Alexandre Bône +4
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a comm…
Anatomically constrained CT image translation for heterogeneous blood vessel segmentation
Giammarco La Barbera, Haithem Boussaid, Francesco Maso +4
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. The combined us…
Optimizing transformations for contrastive learning in a differentiable framework
Camille Ruppli, Pietro Gori, Roberto Ardon +1
Current contrastive learning methods use random transformations sampled from a large list of transformations, with fixed hyperparameters, to learn invariance from an unannotated da…
Is the U-Net Directional-Relationship Aware?
Mateus Riva, Pietro Gori, Florian Yger +1
CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field. However, the nature…
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
Robin Kips, Ruowei Jiang, Sileye Ba +4
Augmented reality applications have rapidly spread across online platforms, allowing consumers to virtually try-on a variety of products, such as makeup, hair dying, or shoes. Howe…
Hair Color Digitization through Imaging and Deep Inverse Graphics
Robin Kips, Panagiotis-Alexandros Bokaris, Matthieu Perrot +2
Hair appearance is a complex phenomenon due to hair geometry and how the light bounces on different hair fibers. For this reason, reproducing a specific hair color in a rendering e…
A deep residual learning implementation of Metamorphosis
Matthis Maillard, Anton François, Joan Glaunès +2
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). However, this…