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Found 12 papers, 8 papers with code
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders.
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability.
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease.
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children.
CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification
Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems.
Pairwise Relation Learning for Semi-supervised Gland Segmentation
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
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.
Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks
Furthermore, the proposed measurements also allow us to show that it is possible to optimise the training process with a new dynamic sampling training approach that continuously and automatically change the mini-batch size and learning rate during the training process.
Competitive Multi-scale Convolution
In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters.
Ranked #17 on
Image Classification
on MNIST
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units
The combination of deep learning models and piecewise linear activation functions allows for the estimation of exponentially complex functions with the use of a large number of subnetworks specialized in the classification of similar input examples.
Ranked #23 on
Image Classification
on MNIST
