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Found 32 papers, 9 papers with code
PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations
This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop.
The Hidden Adversarial Vulnerabilities of Medical Federated Learning
In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks.
Exploring adversarial attacks in federated learning for medical imaging
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
ATCON: Attention Consistency for Vision Models
We propose an unsupervised fine-tuning method that optimizes the consistency of attention maps and show that it improves both classification performance and the quality of attention maps.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i. e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels.
Automated Detection of Patients in Hospital Video Recordings
We evaluate an ImageNet pre-trained Mask R-CNN, a standard deep learning model for object detection, on the task of patient detection using our own curated dataset of 45 videos of hospital patients.
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.
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Optimization plays a key role in the training of deep neural networks.
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
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.
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
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.
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present.
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
We showed that networks optimized using only weak labels reflecting WMH volume generalized better for WMH volume prediction than networks optimized with voxel-wise segmentations of WMH.
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.
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.
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions.
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans.
Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis.
Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
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.
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions.
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.
Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
We compare our method with random forest and a single neural network approach.
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.
Hands-Free Segmentation of Medical Volumes via Binary Inputs
We propose a novel hands-free method to interactively segment 3D medical volumes.
