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Found 55 papers, 23 papers with code
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
What can we learn about a generated image corrupting its latent representation?
The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload.
Anomaly-aware multiple instance learning for rare anemia disorder classification
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations.
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears.
Virtual embeddings and self-consistency for self-supervised learning
We validate TriMix on eight benchmark datasets consisting of natural and medical images with an improvement of 2. 71% and 0. 41% better than the second-best models for both data types.
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation.
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells.
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1. 8% and 15. 8%, respectively.
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
Polyp-artifact relationship analysis using graph inductive learned representations
In inference, we use this classifier to analyze a second graph, generated from artifact and polyp predictions given by region proposal networks.
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years.
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i. e. MS lesions) to large, space-occupying tumors.
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
Medical image segmentation is one of the major challenges addressed by machine learning methods.
The Future of Digital Health with Federated Learning
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.
Fairness by Learning Orthogonal Disentangled Representations
We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization.
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection.
Learn to Estimate Labels Uncertainty for Quality Assurance
Deep Learning sets the state-of-the-art in many challenging tasks showing outstanding performance in a broad range of applications.
Learn to Segment Organs with a Few Bounding Boxes
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology.
Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart.
Uncertainty-based graph convolutional networks for organ segmentation refinement
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability.
Learning Interpretable Features via Adversarially Robust Optimization
In this work, we propose a method for improving the feature interpretability of neural network classifiers.
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.
Learning Interpretable Disentangled Representations using Adversarial VAEs
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice.
Adversarial Networks for Camera Pose Regression and Refinement
Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging task.
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification.
MRI to CT Translation with GANs
We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation.
Self-Attention Equipped Graph Convolutions for Disease Prediction
A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction.
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
GANs for Medical Image Analysis
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification.
Generating Highly Realistic Images of Skin Lesions with GANs
As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models.
Capsule Networks against Medical Imaging Data Challenges
A key component to the success of deep learning is the availability of massive amounts of training data.
Scene Coordinate and Correspondence Learning for Image-Based Localization
Scene coordinate regression has become an essential part of current camera re-localization methods.
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation
By combining the strengths of manifold learning using triplet loss and pose regression, we could either estimate the pose directly reducing the complexity compared to NN search, or use learned descriptor for the NN descriptor matching.
Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction.
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions.
MelanoGANs: High Resolution Skin Lesion Synthesis with GANs
Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images.
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images.
StainGAN: Stain Style Transfer for Digital Histological Images
Digitized Histological diagnosis is in increasing demand.
Semi-Supervised Deep Learning for Fully Convolutional Networks
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious.
X-ray In-Depth Decomposition: Revealing The Latent Structures
X-ray radiography is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures.
