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Found 75 papers, 38 papers with code
Adversarial Data Augmentation via Deformation Statistics
Data augmentation or semi-supervised approaches are commonly used to cope with limited labeled training data.
Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts
In light of this, we reintroduce this dense design into the generalist models, to facilitate the development of generalist models with high segmentation quality.
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos
Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery to obtain better coverage of the organ and detection of various health issues.
A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness
Medical records often consist of different modalities, such as images, text, and tabular information.
NeuralOCT: Airway OCT Analysis via Neural Fields
Our interest in this work is OCT in the context of airway abnormalities in infants and children where the high resolution of OCT and the fact that it is radiation-free is important.
uniGradICON: A Foundation Model for Medical Image Registration
We therefore propose uniGradICON, a first step toward a foundation model for registration providing 1) great performance \emph{across} multiple datasets which is not feasible for current learning-based registration methods, 2) zero-shot capabilities for new registration tasks suitable for different acquisitions, anatomical regions, and modalities compared to the training dataset, and 3) a strong initialization for finetuning on out-of-distribution registration tasks.
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capable of computing dense anatomical correspondences between two images at the voxel level.
Ranked #8 on
Image Registration
on Unpaired-abdomen-CT
Joint Depth Prediction and Semantic Segmentation with Multi-View SAM
Multi-task approaches to joint depth and segmentation prediction are well-studied for monocular images.
$\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration
In contrast to the predominant voxel-based transformation fields used in learning-based registration approaches, NePhi represents deformations functionally, leading to great flexibility within the design space of memory consumption during training and inference, inference time, registration accuracy, as well as transformation regularity.
Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives
We argue that data with complicated deformations can not easily be modeled with point-based registration when only a limited number of points is used to extract influential landmark points.
Multimodal Understanding Through Correlation Maximization and Minimization
To answer 2), we propose novel scores that summarize the learned common and individual structures and visualize the score gradients with respect to the input, visually discerning what the different representations capture.
Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features
Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation.
Inverse Consistency by Construction for Multistep Deep Registration
Inverse consistency is a desirable property for image registration.
MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities
We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities.
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate.
Exploring Cycle Consistency Learning in Interactive Volume Segmentation
Given a medical volume, a user first segments a slice (or several slices) via the interaction module and then propagates the segmentation(s) to the remaining slices.
GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
Harmonization Benchmarking Tool for Neuroimaging Datasets
A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms.
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
Although this design is simple and has been proven effective, it has not yet been explored for interactive image segmentation.
Ranked #2 on
Interactive Segmentation
on SBD
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language
In hopes of enabling compositional generalization, various unsupervised learning algorithms have been proposed with inductive biases that aim to induce compositional structure in learned representations (e. g. disentangled representation and emergent language learning).
Optimal Transport Features for Morphometric Population Analysis
We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease.
PseudoClick: Interactive Image Segmentation with Click Imitation
The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i. e., by a minimal number of user clicks.
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration.
LiftReg: Limited Angle 2D/3D Deformable Registration
We propose LiftReg, a 2D/3D deformable registration approach.
Fluid registration between lung CT and stationary chest tomosynthesis images
We demonstrate our approach for the registration between CT and stationary chest tomosynthesis (sDCT) images and show how it naturally leads to an iterative image reconstruction approach.
On Measuring Excess Capacity in Neural Networks
We study the excess capacity of deep networks in the context of supervised classification.
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment
Atlas building and image registration are important tasks for medical image analysis.
iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images
We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Deep Decomposition for Stochastic Normal-Abnormal Transport
Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields.
Accurate Point Cloud Registration with Robust Optimal Transport
Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration.
ICON: Learning Regular Maps Through Inverse Consistency
We explore if it is possible to obtain spatial regularity using an inverse consistency loss only and elucidate what explains map regularity in such a context.
Dissecting Supervised Contrastive Learning
Minimizing cross-entropy over the softmax scores of a linear map composed with a high-capacity encoder is arguably the most popular choice for training neural networks on supervised learning tasks.
Discovering Hidden Physics Behind Transport Dynamics
To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields.
VoteNet++: Registration Refinement for Multi-Atlas Segmentation
Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images.
Perfusion Imaging: A Data Assimilation Approach
In this work we therefore propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model that best explains the contrast dynamics.
The Fairness-Accuracy Pareto Front
Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms.
A Deep Network for Joint Registration and Reconstruction of Images with Pathologies
They are therefore not designed for the registration of images with strong pathologies for example in the context of brain tumors, and traumatic brain injuries.
Local Temperature Scaling for Probability Calibration
Thus, we propose a learning-based calibration method that focuses on multi-label semantic segmentation.
Robust and Generalizable Visual Representation Learning via Random Convolutions
In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation.
Ranked #110 on
Domain Generalization
on PACS
Anatomical Data Augmentation via Fluid-based Image Registration
We introduce a fluid-based image augmentation method for medical image analysis.
A Shooting Formulation of Deep Learning
Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE).
Deep Goal-Oriented Clustering
Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning, respectively.
Topologically Densified Distributions
We study regularization in the context of small sample-size learning with over-parameterized neural networks.
VoteNet+ : An Improved Deep Learning Label Fusion Method for Multi-atlas Segmentation
Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF.
The fairness-accuracy landscape of neural classifiers
That machine learning algorithms can demonstrate bias is well-documented by now.
Deep Message Passing on Sets
DMPS not only connects learning on graphs with learning on sets via deep kernel learning, but it also bridges message passing on sets and traditional diffusion dynamics commonly used in denoising models.
Deep Multi-View Learning via Task-Optimal CCA
Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels.
Connectivity-Optimized Representation Learning via Persistent Homology
In particular, we control the connectivity of an autoencoder's latent space via a novel type of loss, operating on information from persistent homology.
Region-specific Diffeomorphic Metric Mapping
We introduce a region-specific diffeomorphic metric mapping (RDMM) registration approach.
Ranked #1 on
Image Registration
on Osteoarthritis Initiative
Graph Filtration Learning
We propose an approach to learning with graph-structured data in the problem domain of graph classification.
Metric Learning for Image Registration
Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.
Ranked #1 on
Diffeomorphic Medical Image Registration
on CUMC12
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+2
VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach.
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores (%) over an unsupervised registration network by 2. 7 and 1. 8 on the knee and brain images respectively.
Networks for Joint Affine and Non-parametric Image Registration
In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model.
Ranked #2 on
Image Registration
on Osteoarthritis Initiative
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology
We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function.
Stochastic Block Models with Multiple Continuous Attributes
While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes.
Fast Predictive Simple Geodesic Regression
Deformable image registration and regression are important tasks in medical image analysis.
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Extracting the brain from images with strong pathologies, for example, the presence of a tumor or of a traumatic brain injury, is challenging.
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features.
Deep Learning with Topological Signatures
Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems.
AGA: Attribute-Guided Augmentation
We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner.
Compressing networks with super nodes
Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns.
Social and Information Networks Physics and Society
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies.
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
A deep encoder-decoder network is used as the prediction model.
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation prediction from multimodal images.
Probabilistic Diffeomorphic Registration: Representing Uncertainty
The Bayesian posterior distribution over the deformations aligning a moving and a fixed image is approximated via a variational formulation.
AGA: Attribute Guided Augmentation
We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner.
Fast Predictive Image Registration
We present a method to predict image deformations based on patch-wise image appearance.
One-Shot Learning of Scene Locations via Feature Trajectory Transfer
In particular, we leverage a recently introduced dataset with fine-grain annotations to estimate feature trajectories for a collection of transient attributes and then show how these trajectories can be transferred to new image representations.
Statistical Topological Data Analysis - A Kernel Perspective
We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data.
Parametric Regression on the Grassmannian
We then specialize this idea to the Grassmann manifold and demonstrate that it yields a simple, extensible and easy-to-implement solution to the parametric regression problem.
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary.
Multi-modal Image Registration for Correlative Microscopy
In this report, I introduce two methods of image registration for correlative microscopy.
Scene Parsing with Object Instances and Occlusion Ordering
This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships.
