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Found 56 papers, 17 papers with code
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning.
Segmenting Cardiac Muscle Z-disks with Deep Neural Networks
In this study, we apply deep learning based segmentation models to extract Z-disks in images of striated muscle tissue.
Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping
Artificial intelligence has found increasing use for ovarian cancer morphological subtyping from histopathology slides, but the optimal magnification for computational interpretation is unclear.
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function.
Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation
Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images.
Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
While recent deep-learning approaches for automated report generation from medical images have seen some success, most studies have relied on image-derived features alone, ignoring non-imaging patient data.
Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning
For some therapies, it is not possible to predict patients' responses, potentially exposing them to the adverse effects of treatment without any therapeutic benefit.
Learned Local Attention Maps for Synthesising Vessel Segmentations
We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI.
Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain.
Adaptive Semi-Supervised Segmentation of Brain Vessels with Ambiguous Labels
Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment.
Generative Adversarial Networks for Stain Normalisation in Histopathology
The rapid growth of digital pathology in recent years has provided an ideal opportunity for the development of artificial intelligence-based tools to improve the accuracy and efficiency of clinical diagnoses.
GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration
In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses.
A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy
The generation of virtual populations (VPs) of anatomy is essential for conducting in silico trials of medical devices.
Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review
The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer.
Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network
The fairness of a deep neural network is strongly affected by dataset bias and spurious correlations, both of which are usually present in modern feature-rich and complex visual datasets.
Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning
Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process.
Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE).
Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images
In such scenarios, enforcing smooth, globally continuous deformation fields leads to incorrect/implausible registration results.
A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras
Specifically, we propose a generative shape compositional framework which comprises two components - a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population; and a spatial composition network which assembles/composes the structures synthesised by the former into multi-part shape assemblies (viz.
Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI
We extend previous studies accounting for the cardiomyocyte shape variability, water exchange between the cardiomyocytes (intercalated discs), myocardial microstructure disarray, and four sheetlet orientations.
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound
First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space.
Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework
Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.
Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation
While various methods have been proposed to mitigate such bias, the majority require explicit knowledge of the biases present in the training data in order to mitigate.
Mitosis domain generalization in histopathology images -- The MIDOG challenge
The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms.
The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays
Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak.
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods.
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
Flip Learning: Erase to Segment
Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation.
Style Curriculum Learning for Robust Medical Image Segmentation
A style transfer model with style fusion is employed to generate the curriculum samples.
Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction
In this paper, we propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
A Deep Discontinuity-Preserving Image Registration Network
Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions.
CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning
Image registration is a fundamental building block for various applications in medical image analysis.
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces.
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence.
Fed-Sim: Federated Simulation for Medical Imaging
Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.
Partially Conditioned Generative Adversarial Networks
With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
In this study, we investigate the impact of various data augmentation algorithms, e. g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem.
COPD Classification in CT Images Using a 3D Convolutional Neural Network
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world.
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach
In this work, we propose an imitation learning framework for the registration of 2D color funduscopic images for a wide range of applications such as disease monitoring, image stitching and super-resolution.
Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics
A second approach is based on deep learning and relies on centerline extraction as sole prerequisite.
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features.
A Divide-and-Conquer Approach towards Understanding Deep Networks
We start with a high-performance U-Net and show by step-by-step conversion that we are able to divide the network into modules of known operators.
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer.
Dilated deeply supervised networks for hippocampus segmentation in MRI
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).
Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge.
A Multi-task Framework for Skin Lesion Detection and Segmentation
This underlines the need for an accurate and automatic approach to skin lesion segmentation.
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
Early detection and segmentation of skin lesions are crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients.
Action Learning for 3D Point Cloud Based Organ Segmentation
From this we conclude that our method is robust, and we believe that our method can be successfully applied to many more applications, in particular, in the interventional imaging space.
Classification of breast cancer histology images using transfer learning
Breast cancer is one of the leading causes of mortality in women.
Classification
Classification Of Breast Cancer Histology Images
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Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors.
Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
The proposed method achieved an average Dice coefficient of 0. 7808$\pm$0. 1729 and Jaccard index of 0. 6704$\pm$0. 2167.
Frangi-Net: A Neural Network Approach to Vessel Segmentation
In this paper, we reformulate the conventional 2-D Frangi vesselness measure into a pre-weighted neural network ("Frangi-Net"), and illustrate that the Frangi-Net is equivalent to the original Frangi filter.
