Search Results for author:
Found 14 papers, 4 papers with code
Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CT
To evaluate the method, we compared manual and automatic assessment (computed using ten-fold cross-validation) with respect to 1) the agreement with an independent observer's assessment (available in a random subset of 47 scans); 2) the accuracy in delineating ICAC as judged via blinded visual comparison by an expert; 3) the association with first stroke incidence from the scan date until 2012.
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images
Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting.
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations
In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images.
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles.
Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation
The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes.
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.
Deep Learning from Label Proportions for Emphysema Quantification
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue.
Hydranet: Data Augmentation for Regression Neural Networks
The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0. 73 on the first task and 0. 84 on the second task, only using between 25 and 30 scans with a single global label per scan for training.
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count.
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.
