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Found 11 papers, 6 papers with code
Deep Learning-Based Automated Post-Operative Gross Tumor Volume Segmentation in Glioblastoma Patients
Our proposed double U-Net model trained with input sequences [T1, T1C, FL + T2, T1C] achieved a better mean Dice score of 0. 8585 and Hausdorff distance of 4. 1942 compared to all the baseline models and ensemble models trained.
Two Stage Segmentation of Cervical Tumors using PocketNet
Cervical cancer remains the fourth most common malignancy amongst women worldwide. 1 Concurrent chemoradiotherapy (CRT) serves as the mainstay definitive treatment regimen for locally advanced cervical cancers and includes external beam radiation followed by brachytherapy. 2 Integral to radiotherapy treatment planning is the routine contouring of both the target tumor at the level of the cervix, associated gynecologic anatomy and the adjacent organs at risk (OARs).
MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework
To address this, we introduce the Medical Imaging Segmentation Toolkit (MIST), a simple, modular, and end-to-end medical imaging segmentation framework designed to facilitate consistent training, testing, and evaluation of deep learning-based medical imaging segmentation methods.
Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equations (PDEs).
Distributed Conditional GAN (discGAN) For Synthetic Healthcare Data Generation
Modeling distributions of discrete and continuous tabular data is a non-trivial task with high utility.
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation
Accurate medical imaging segmentation is critical for precise and effective medical interventions.
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
Within medical imaging segmentation, the Dice coefficient and Hausdorff-based metrics are standard measures of success for deep learning models.
Automation of Radiation Treatment Planning for Rectal Cancer
A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields.
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
Deep neural networks with multilevel connections process input data in complex ways to learn the information. A networks learning efficiency depends not only on the complex neural network architecture but also on the input training images. Medical image segmentation with deep neural networks for skull stripping or tumor segmentation from magnetic resonance images enables learning both global and local features of the images. Though medical images are collected in a controlled environment, there may be artifacts or equipment based variance that cause inherent bias in the input set. In this study, we investigated the correlation between the image quality metrics of MR images with the neural network segmentation accuracy. For that we have used the 3D DenseNet architecture and let the network trained on the same input but applying different methodologies to select the training data set based on the IQM values. The difference in the segmentation accuracy between models based on the random training inputs with IQM based training inputs shed light on the role of image quality metrics on segmentation accuracy. By running the image quality metrics to choose the training inputs, further we may tune the learning efficiency of the network and the segmentation accuracy.
PocketNet: A Smaller Neural Network for Medical Image Analysis
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
