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Found 14 papers, 3 papers with code
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
CFCM: Segmentation via Coarse to Fine Context Memory
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.
NeurReg: Neural Registration and Its Application to Image Segmentation
Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.
TOMAAT: volumetric medical image analysis as a cloud service
Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems.
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting.
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs).
Straight to the point: reinforcement learning for user guidance in ultrasound
Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders.
Interactive segmentation of medical images through fully convolutional neural networks
Semi-automated approaches keep users in control of the results by providing means for interaction, but the main challenge is to offer a good trade-off between precision and required interaction.
Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network
Radiogenomic map linking image features and gene expression profiles is useful for noninvasively identifying molecular properties of a particular type of disease.
Weakly supervised segmentation from extreme points
Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images.
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.
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation
For example, fully convolutional neural networks (FCN) achieve the state-of-the-art performance in several applications of 2D/3D medical image segmentation.
Correlation via Synthesis: End-to-end Image Generation and Radiogenomic Learning Based on Generative Adversarial Network
In this work, we investigate the potential of an end-to-end method fusing gene code with image features to generate synthetic pathology image and learn radiogenomic map simultaneously.
