Search Results for author:
Found 47 papers, 15 papers with code
Cellular EXchange Imaging (CEXI): Evaluation of a diffusion model including water exchange in cells using numerical phantoms of permeable spheres
Conclusion: This study highlights the importance of modeling the exchange time to accurately quantify microstructure properties in permeable cellular substrates.
Adaptive Similarity Bootstrapping for Self-Distillation
Most self-supervised methods for representation learning leverage a cross-view consistency objective i. e. they maximize the representation similarity of a given image's augmented views.
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other.
Ranked #4 on
Unsupervised Semantic Segmentation
on COCO-Stuff
TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation
Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain.
Self-Supervised Learning-Based Cervical Cytology Diagnostics in Low-Data Regime and Low-Resource Setting
Screening Papanicolaou test samples effectively reduces cervical cancer-related mortality, but the lack of trained cytopathologists prevents its widespread adoption in low-resource settings.
Microstructure estimation from diffusion-MRI: Compartmentalized models in permeable cellular tissue
Finally, the simulations performed in this work showed that the exchange between the intracellular and the extracellular space cannot be neglected in permeable tissue with a conventional PGSE sequence, to obtain accurate estimates.
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform
Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge.
ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
We further introduce a novel mixing data-augmentation, namely ScoreMix, by leveraging the image's semantic distribution to guide the data mixing and produce coherent sample-label pairs.
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability.
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
Recently, advanced MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis.
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
Motivated by atlas-based segmentation, we propose a novel volumetric self-supervised learning for data augmentation capable of synthesizing volumetric image-segmentation pairs via learning transformations from a single labeled atlas to the unlabeled data.
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets.
Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection
Alternative MRI techniques exhibit a higher level of sensitivity to focal and diffuse MS pathology than conventional MRI acquisitions.
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images
Addressing both issues, we propose a simple yet universal approach to improve the perceptual quality of the HR prediction from a pre-trained SR network on a given LR input by further fine-tuning the SR network on a subset of images from the training dataset with similar patterns of activation as the initial HR prediction, with respect to the filters of a feature extractor.
Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation
We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.
Hierarchical Graph Representations in Digital Pathology
We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions.
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.
Self-Supervised Anomaly Detection
Semi-supervised Anomaly Detection
+1
Quantifying Explainers of Graph Neural Networks in Computational Pathology
However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists.
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario.
CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking
Thanks to its capability of acquiring full-view frames at multiple kilohertz, ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical phenomena in the human body, with pioneering applications such as ultrasensitive flow imaging in the cardiovascular system or shear-wave elastography.
CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging
In vitro and in vivo experiments show that trainings carried out on simulated data perform well in experimental settings.
Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI
The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention.
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues.
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer.
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs.
Towards Explainable Graph Representations in Digital Pathology
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes.
General Classification
Histopathological Image Classification
+1
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
Revisiting Few-Shot Learning for Facial Expression Recognition
Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest impact on performance.
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart.
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem.
Exploring Factors for Improving Low Resolution Face Recognition
By leveraging this information, we have utilized deep face models trained on MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms.
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.
Learn to synthesize and synthesize to learn
To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest.
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings.
Ranked #27 on
Graph Classification
on MUTAG
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
We introduce a new scene graph generation method called image-level attentional context modeling (ILAC).
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients.
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity.
Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times.
Robust Real-Time Multi-View Eye Tracking
Despite significant advances in improving the gaze tracking accuracy under controlled conditions, the tracking robustness under real-world conditions, such as large head pose and movements, use of eyeglasses, illumination and eye type variations, remains a major challenge in eye tracking.
A Computer Vision System to Localize and Classify Wastes on the Streets
Littering quantification is an important step for improving cleanliness of cities.
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available.
Combining Multiple Views for Visual Speech Recognition
In this paper, we explore this aspect and provide a comprehensive study on combining multiple views for visual speech recognition.
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches.
MBIS: Multivariate Bayesian Image Segmentation Tool
MBIS supports multi-channel bias field correction based on a B-spline model.
