188 citations · 400 across the 22 of their papers we have counts for
28 papers
Certification of Deep Learning Models for Medical Image Segmentation
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +3
In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification mode…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
Joseph Boyd, Irène Villa, Marie-Christine Mathieu +4
In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue land…
MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
Théo Estienne, Maria Vakalopoulou, Enzo Battistella +5
Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made d…
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
Joseph Boyd, Mykola Liashuha, Eric Deutsch +3
The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such imag…
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment
Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon +3
Sarcopenia is a medical condition characterized by a reduction in muscle mass and function. A quantitative diagnosis technique consists of localizing the CT slice passing through t…
Exploring Deep Registration Latent Spaces
Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis +8
Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretabilit…
Weakly supervised pan-cancer segmentation tool
Marvin Lerousseau, Marion Classe, Enzo Battistella +8
The vast majority of semantic segmentation approaches rely on pixel-level annotations that are tedious and time consuming to obtain and suffer from significant inter and intra-expe…
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification
Marvin Lerousseau, Maria Vakalopoulou, Eric Deutsch +1
Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the interdependencies of tiles extracte…
Cancer Gene Profiling through Unsupervised Discovery
Enzo Battistella, Maria Vakalopoulou, Roger Sun +9
Precision medicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes as well as the…
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
Theophraste Henry, Alexandre Carre, Marvin Lerousseau +4
Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly…
Multimodal brain tumor classification
Marvin Lerousseau, Eric Deutsh, Nikos Paragios
Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathologi…
Deep learning based registration using spatial gradients and noisy segmentation labels
Théo Estienne, Maria Vakalopoulou, Enzo Battistella +6
Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and p…
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
Mihir Sahasrabudhe, Stergios Christodoulidis, Roberto Salgado +5
Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These meth…
AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella +28
Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity. The standard of referenc…
Weakly supervised multiple instance learning histopathological tumor segmentation
Marvin Lerousseau, Maria Vakalopoulou, Marion Classe +7
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on…
Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration
Enzo Ferrante, Puneet K. Dokania, Rafael Marini Silva +1
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with…
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Stergios Christodoulidis, Mihir Sahasrabudhe, Maria Vakalopoulou +4
Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a n…
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
Zhixin Shu, Mihir Sahasrabudhe, Alp Guler +3
In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template par…
Continuous Relaxation of MAP Inference: A Nonconvex Perspective
D. Khuê Lê-Huu, Nikos Paragios
In this paper, we study a nonconvex continuous relaxation of MAP inference in discrete Markov random fields (MRFs). We show that for arbitrary MRFs, this relaxation is tight, and a…
Newton-type Methods for Inference in Higher-Order Markov Random Fields
Hariprasad Kannan, Nikos Komodakis, Nikos Paragios
Linear programming relaxations are central to {\sc map} inference in discrete Markov Random Fields. The ability to properly solve the Lagrangian dual is a critical component of suc…
Deformable Registration through Learning of Context-Specific Metric Aggregation
Enzo Ferrante, Puneet K Dokania, Rafael Marini +1
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conven…
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
Afshine Amidi, Shervine Amidi, Dimitrios Vlachakis +3
During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to t…
Slice-to-volume medical image registration: a survey
Enzo Ferrante, Nikos Paragios
During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art…