Search Results for author:
Found 15 papers, 8 papers with code
Giga-SSL: Self-Supervised Learning for Gigapixel Images
Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice.
MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network
Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process.
Exploring Deep Registration Latent Spaces
Explainability of deep neural networks is one of the most challenging and interesting problems in the field.
Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data
The pathological analysis of biopsy specimens is essential to cancer diagnosis, treatment selection and prognosis.
Weakly supervised pan-cancer segmentation tool
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-expert variability.
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is the preferred approach for whole slide image classification.
Design and implementation of an environment for Learning to Run a Power Network (L2RPN)
This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics.
Cancer Gene Profiling through Unsupervised Discovery
Precision medicine is a paradigm shift in healthcare relying heavily on genomics data.
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
Brain tumor segmentation is a critical task for patient's disease management.
Deep learning based registration using spatial gradients and noisy segmentation labels
Image registration is one of the most challenging problems in medical image analysis.
Multimodal brain tumor classification
validation) balanced-accuracy, kappa and f1 of 0. 913, 0. 897 and 0. 951 (resp.
Weakly supervised multiple instance learning histopathological tumor segmentation
In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems.
Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation
Image registration and segmentation are the two most studied problems in medical image analysis.
Learning to run a power network challenge for training topology controllers
For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities.
U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets
We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks.
