Search Results for author:
Found 36 papers, 18 papers with code
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
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 their excellent performance on computer vision problems.
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.
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner.
Ranked #9 on
Unsupervised Facial Landmark Detection
on MAFL
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.
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
We show that the identification of the magnification level for tiles can generate a preliminary self-supervision signal to locate nuclei.
Deep learning based registration using spatial gradients and noisy segmentation labels
Image registration is one of the most challenging problems in medical image analysis.
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.
Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation
Image registration and segmentation are the two most studied problems in medical image analysis.
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells.
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.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Alternating Direction Graph Matching
In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions.
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Image registration and in particular deformable registration methods are pillars of medical imaging.
Continuous Relaxation of MAP Inference: A Nonconvex Perspective
In this paper, we study a nonconvex continuous relaxation of MAP inference in discrete Markov random fields (MRFs).
Newton-type Methods for Inference in Higher-Order Markov Random Fields
Linear programming relaxations are central to {\sc map} inference in discrete Markov Random Fields.
Deformable Registration through Learning of Context-Specific Metric Aggregation
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures.
Slice-to-volume medical image registration: a survey
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 and enabled the development of novel medical procedures.
Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction.
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation.
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data.
Discriminative Parameter Estimation for Random Walks Segmentation
However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned.
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report
However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned.
Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration
Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images.
Sparse and Locally Constant Gaussian Graphical Models
Locality information is crucial in datasets where each variable corresponds to a measurement in a manifold (silhouettes, motion trajectories, 2D and 3D images).
Clustering via LP-based Stabilities
To deal with the most critical issue in a center-based clustering algorithm (selection of cluster centers), we also introduce the notion of stability of a cluster center, which is a well defined LP-based quantity that plays a key role to our algorithm's success.
AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity.
Multimodal brain tumor classification
validation) balanced-accuracy, kappa and f1 of 0. 913, 0. 897 and 0. 951 (resp.
Cancer Gene Profiling through Unsupervised Discovery
Precision medicine is a paradigm shift in healthcare relying heavily on genomics data.
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.
Exploring Deep Registration Latent Spaces
Explainability of deep neural networks is one of the most challenging and interesting problems in the field.
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment
In this paper, we propose a deep reinforcement learning method for accurate localization of the L3 CT slice.
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.
Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data
The pathological analysis of biopsy specimens is essential to cancer diagnosis, treatment selection and prognosis.
Certification of Deep Learning Models for Medical Image Segmentation
In this paper, we present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models.
