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Found 20 papers, 10 papers with code
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).
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.
Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema
For the task of grading DR, on the test data (n=56), the ensemble achieved an accuracy of 83. 9\%, while for the task for grading macular edema the network achieved an accuracy of 95. 45% (n=44).
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).
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.
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.
Zero Shot Learning for Multi-Modal Real Time Image Registration
In this report we present an unsupervised image registration framework, using a pre-trained deep neural network as a feature extractor.
Medical Image Registration
Unsupervised Image Registration
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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.
Dynamic Regularizer with an Informative Prior
Both the regularizers mentioned above are formulated by assuming certain priors in the parameter space and these assumptions, in some cases, induce sparsity in the parameter space.
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.
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.
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.
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.
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.
GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
Specifically, we provide a generator to visualize the `effect' of interactions among features in latent space and draw feature importance therefrom as local explanations.
Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models
We achieve this by using the natural properties of \emph{hyperbolic geometry} to more efficiently model a hierarchy of symbolic features and generate \emph{hierarchical symbolic rules} as part of our explanations.
Vector Quantisation for Robust Segmentation
The reliability of segmentation models in the medical domain depends on the model's robustness to perturbations in the input space.
Explaining Image Classification with Visual Debates
An effective way to obtain different perspectives on any given topic is by conducting a debate, where participants argue for and against the topic.
A Causal Ordering Prior for Unsupervised Representation Learning
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables.
Grounded Object Centric Learning
The extraction of modular object-centric representations for downstream tasks is an emerging area of research.
