no code implementations • 22 Aug 2023 • Rebeca Vétil, Clément Abi-Nader, Alexandre Bône, Marie-Pierre Vullierme, Marc-Michel Rohé, Pietro Gori, Isabelle Bloch
We address the problem of learning Deep Learning Radiomics (DLR) that are not redundant with Hand-Crafted Radiomics (HCR).
1 code implementation • 18 Aug 2023 • Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch
Early diagnosis of prostate cancer is crucial for efficient treatment.
1 code implementation • 10 Jul 2023 • Emma Sarfati, Alexandre Bône, Marc-Michel Rohé, Pietro Gori, Isabelle Bloch
Large medical imaging datasets can be cheaply and quickly annotated with low-confidence, weak labels (e. g., radiological scores).
1 code implementation • 11 May 2023 • Hugo Oliveira, Pedro H. T. Gama, Isabelle Bloch, Roberto Marcondes Cesar Jr
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.
no code implementations • 8 Mar 2023 • Marc Aiguier, Isabelle Bloch, Salim Nibouche, Ramon Pino Perez
Then we introduce the notion of structuring neighborhoods, and show that the dilations and erosions based on them lead to a constructive modal logic, for which a sound and complete proof system is proposed.
no code implementations • 16 Feb 2023 • Emma Sarfati, Alexandre Bone, Marc-Michel Rohe, Pietro Gori, Isabelle Bloch
Identifying cirrhosis is key to correctly assess the health of the liver.
1 code implementation • 18 Jan 2023 • Jérémy Chopin, Jean-Baptiste Fasquel, Harold Mouchère, Rozenn Dahyot, Isabelle Bloch
On FASSEG data, results show that our module improves accuracy of the CNN by about 6. 3% (the Hausdorff distance decreases from 22. 11 to 20. 71).
no code implementations • 21 Oct 2022 • Rebeca Vétil, Clément Abi Nader, Alexandre Bône, Marie-Pierre Vullierme, Marc-Michel Roheé, Pietro Gori, Isabelle Bloch
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs.
no code implementations • 4 Oct 2022 • Giammarco La Barbera, Haithem Boussaid, Francesco Maso, Sabine Sarnacki, Laurence Rouet, Pietro Gori, Isabelle Bloch
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.
no code implementations • 28 Jul 2022 • Samy Blusseau, Santiago Velasco-Forero, Jesus Angulo, Isabelle Bloch
In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector.
no code implementations • 27 Jul 2022 • Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch
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.
1 code implementation • 6 Jul 2022 • Mateus Riva, Pietro Gori, Florian Yger, Isabelle Bloch
CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field.
no code implementations • 12 May 2022 • Robin Kips, Ruowei Jiang, Sileye Ba, Brendan Duke, Matthieu Perrot, Pietro Gori, Isabelle Bloch
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.
no code implementations • 8 Feb 2022 • Robin Kips, Panagiotis-Alexandros Bokaris, Matthieu Perrot, Pietro Gori, Isabelle Bloch
Since rendering realistic hair images requires path-tracing rendering, the conventional inverse graphics approach based on differentiable rendering is untractable.
no code implementations • 1 Feb 2022 • Matthis Maillard, Anton François, Joan Glaunès, Isabelle Bloch, Pietro Gori
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).
no code implementations • 6 Jul 2021 • Giammarco La Barbera, Pietro Gori, Haithem Boussaid, Bruno Belucci, Alessandro Delmonte, Jeanne Goulin, Sabine Sarnacki, Laurence Rouet, Isabelle Bloch
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.
1 code implementation • 5 Jul 2021 • Mateus Riva, Florian Yger, Pietro Gori, Roberto M. Cesar Jr., Isabelle Bloch
We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities).
no code implementations • 17 Jun 2021 • Minhao Hu, Matthis Maillard, Ya zhang, Tommaso Ciceri, Giammarco La Barbera, Isabelle Bloch, Pietro Gori
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).
no code implementations • 12 May 2021 • Robin Kips, Ruowei Jiang, Sileye Ba, Edmund Phung, Parham Aarabi, Pietro Gori, Matthieu Perrot, Isabelle Bloch
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.
no code implementations • 22 Feb 2021 • Mateus Riva, Pietro Gori, Florian Yger, Roberto Cesar, Isabelle Bloch
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.
no code implementations • 18 Jan 2021 • Vincent Couteaux, Mathilde Trintignac, Olivier Nempont, Guillaume Pizaine, Anna Sesilia Vlachomitrou, Pierre-Jean Valette, Laurent Milot, Isabelle Bloch
We address the problem of multimodal liver segmentation in paired but unregistered T1 and T2-weighted MR images.
2 code implementations • 4 Dec 2020 • Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, Isabelle Bloch
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks.
Ranked #132 on Image Classification on CIFAR-10
no code implementations • 29 Sep 2020 • Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, Saïd Ladjal, Isabelle Bloch
We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context.
no code implementations • 24 Aug 2020 • Robin Kips, Pietro Gori, Matthieu Perrot, Isabelle Bloch
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.
no code implementations • 1 Jun 2020 • Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, Isabelle Bloch
This is due to the fact that modern DNNs are usually uncalibrated and we cannot characterize their epistemic uncertainty.
no code implementations • 20 Mar 2020 • Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch
This report is about applications based on spatial-frequency transform and deep learning techniques.
no code implementations • ECCV 2020 • Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, Isabelle Bloch
During training, the weights of a Deep Neural Network (DNN) are optimized from a random initialization towards a nearly optimum value minimizing a loss function.
no code implementations • 20 Mar 2019 • Bastien Ponchon, Santiago Velasco-Forero, Samy Blusseau, Jesus Angulo, Isabelle Bloch
This paper addresses the issue of building a part-based representation of a dataset of images.
1 code implementation • 19 Mar 2019 • Yunxiang Zhang, Samy Blusseau, Santiago Velasco-Forero, Isabelle Bloch, Jesus Angulo
Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks.
no code implementations • 5 Mar 2018 • Marc Aiguier, Jamal Atif, Isabelle Bloch, Ramón Pino-Pérez
The aim of this paper is to introduce a new framework for defining abductive reasoning operators based on a notion of retraction in arbitrary logics defined as satisfaction systems.
no code implementations • 14 Feb 2018 • Isabelle Bloch, Jérôme Lang, Ramón Pino Pérez, Carlos Uzcátegui
Several tasks in artificial intelligence require to be able to find models about knowledge dynamics.
no code implementations • 16 Jan 2017 • Hadrien Bertrand, Matthieu Perrot, Roberto Ardon, Isabelle Bloch
Improving the model is not an easy task, due to the large number of hyper-parameters governing both the architecture and the training of the network, and to the limited understanding of their relevance.
1 code implementation • 19 Dec 2016 • Henrique Morimitsu, Isabelle Bloch, Roberto M. Cesar-Jr
In this paper, we propose a novel approach for exploiting structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion.
no code implementations • 26 Feb 2015 • Marc Aiguier, Jamal Atif, Isabelle Bloch, Céline Hudelot
In this paper we address both the generalization of the well-known AGM postulates, and the definition of concrete and well-founded revision operators in different DL families.
no code implementations • 8 Feb 2015 • Marc Aiguier, Jamal Atif, Isabelle Bloch, Céline Hudelot
Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics.