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Found 18 papers, 3 papers with code
MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
Survival risk stratification is an important step in clinical decision making for breast cancer management.
BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion
Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to achieve a holistic patient profile and perform survival risk stratification of ER+ breast cancer patients.
Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning
Methods: We propose an automatic three-dimensional multi-modal (B-mode and power Doppler) ultrasound segmentation of the human placenta using deep learning combined with different fusion strategies. We collected data containing Bmode and power Doppler ultrasound scans for 400 studies.
Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation
Coronary artery diseases are among the leading causes of mortality worldwide.
Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images
Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance.
Visual Question Answering in the Medical Domain
Medical visual question answering (Med-VQA) is a machine learning task that aims to create a system that can answer natural language questions based on given medical images.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images
Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC).
Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries
Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease).
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease
This method uses temporal abstraction to convert the data into a more appropriate form for modelling, temporal pattern mining, to discover patterns in the complex, longitudinal data and machine learning models of survival analysis to select the discovered patterns.
Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers
When feature selection is applied to these datasets to identify the most important features, the biases inherent in some multivariate feature selectors due to correlated features make it difficult for these methods to distinguish between the important and irrelevant features and the results of the feature selection process can be unstable.
Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery
A fixed threshold, which is typically applied, offers no guarantee that the final set of selected features contains only relevant features.
Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis
However, in CT and CXR images, the infected area occupies only a small part of the image.
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning
The results of experiments on localisation of the Spleen, Left and Right Kidneys in CT Images using supervised and semi supervised learning (SSL) demonstrate the ability to address data limitations with a much smaller data set and fewer annotations, compared to other state-of-the-art methods.
Video Generative Adversarial Networks: A Review
Then, a comprehensive review of video GANs models is provided under two main divisions according to the presence or non-presence of a condition.
Brain tumour segmentation using cascaded 3D densely-connected U-net
In this paper, we propose a deep-learning based method to segment a brain tumour into its subregions: whole tumour, tumour core and enhancing tumour.
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation
In this paper, we consider the sigmoid Gaussian Hawkes process model: the baseline intensity and triggering kernel of Hawkes process are both modeled as the sigmoid transformation of random trajectories drawn from Gaussian processes (GP).
Efficient EM-Variational Inference for Hawkes Process
In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility.
