Search Results for author:
Found 17 papers, 8 papers with code
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation
Generating annotations is laborious and time-consuming for human experts, especially in medical image segmentation.
PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net
In this work, we present our proposed method to segment the pulmonary arteries from the CT scans using Swin UNETR and U-Net-based deep neural network architecture.
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture
On the BraTS2020 validation data (n = 125), this architecture achieved a tumor core, whole tumor, and active tumor dice of 0. 744, 0. 876, 0. 714, respectively.
Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability
Extracting such a graphical representation of the model's behavior on an abstract, higher conceptual level would unravel the learnings of these models and would help us to evaluate the steps taken by the model for predictions.
Distangling Biological Noise in Cellular Images with a focus on Explainability
One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market.
Structurally aware bidirectional unpaired image to image translation between CT and MR
Deep learning techniques like generative adversarial networks (GANs) can help us to leverage the possibility of an image to image translation between multiple imaging modalities, which in turn helps in saving time and cost.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis.
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis
In this paper, we explore various techniques to explain the functional organization of brain tumor segmentation models and to extract visualizations of internal concepts to understand how these networks achieve highly accurate tumor segmentations.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
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.
Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs
In this manuscript, we present a robust method for glaucoma screening from fundus images using an ensemble of convolutional neural networks (CNNs).
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers
Our approach in ACDC-2017 challenge stands second place for segmentation and first place in automated cardiac disease diagnosis tasks with an accuracy of 100%.
Enhanced Image Classification With Data Augmentation Using Position Coordinates
In this paper we propose the use of image pixel position coordinate system to improve image classification accuracy in various applications.
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet).
Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis
In this paper, we use a fully convolutional neural network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI).
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
The ND learns the non-lesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.
