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Found 29 papers, 17 papers with code
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography
The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details.
Federated Impression for Learning with Distributed Heterogeneous Data
Through experimentation in this study, we show that data heterogeneity leads to the phenomenon of catastrophic forgetting during local training.
Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection
This distribution shift significantly impacts the model's robustness, posing major challenge to clinical deployment.
CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning
Subsequently, we update the network by combining the synthesized data with new class data and incorporate several losses, including an intra-domain contrastive loss to generalize the deep network trained on the synthesized data to real data, a margin loss to increase separation among previous classes and new ones, and a cosine-normalized cross-entropy loss to alleviate the adverse effects of imbalanced distributions in training data.
Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification.
GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis
Due to inter-observer variability in echo-based diagnosis, which arises from the variability in echo image acquisition and the interpretation of echo images based on clinical experience, vision-based machine learning (ML) methods have gained popularity to act as secondary layers of verification.
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise
However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set.
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.
ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography
Aortic stenosis (AS) is a common heart valve disease that requires accurate and timely diagnosis for appropriate treatment.
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms
To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD).
TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision
We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature.
Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound
B-line artifacts in LUS videos are key findings associated with pulmonary congestion.
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound
Our method outperforms baseline supervised learning approaches, generalizes well between different data centers, and scale well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data.
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos.
Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound
We train a deep model using a co-teaching paradigm to handle noise in labels, together with an evidential deep learning method for uncertainty estimation.
Class Impression for Data-free Incremental Learning
Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a \ours.
Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements.
U-LanD: Uncertainty-Driven Video Landmark Detection
We tackle a specifically challenging problem, where training labels are noisy and highly sparse.
Reciprocal Landmark Detection and Tracking with Extremely Few Annotations
The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames.
PEP: Parameter Ensembling by Perturbation
In most experiments, PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration.
Variational Learning with Disentanglement-PyTorch
Unsupervised learning of disentangled representations is an open problem in machine learning.
A Study into Echocardiography View Conversion
The size and length of the left ventricle in the generated target echo view is compared against that of the target ground-truth to assess the validity of the echo view conversion.
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
We make the following contributions: 1) We systematically compare cross entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples.
A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics
However, a qualitative study of the encoded latents reveal that there is not a consistent correlation between the reported metrics and the disentanglement potential of the model.
GAN-enhanced Conditional Echocardiogram Generation
One of the most sought-after problems in echo is the segmentation of cardiac structures (e. g. chambers).
On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessment
Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy.
Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery
The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team.
Deep Neural Maps
We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM).
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
