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Found 81 papers, 34 papers with code
LaPA: Latent Prompt Assist Model For Medical Visual Question Answering
In this paper, we propose the Latent Prompt Assist model (LaPA) for medical visual question answering.
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
Overall, our results indicate that the shape of the brain's connections is predictive of human language function.
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID).
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling
Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature.
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy.
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation
Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles hindering the application of deep learning models for nuclei analysis, which holds a broad spectrum of potential applications in digital pathology.
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance.
Complex Organ Mask Guided Radiology Report Generation
The goal of automatic report generation is to generate a clinically accurate and coherent phrase from a single given X-ray image, which could alleviate the workload of traditional radiology reporting.
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection
Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in autonomous driving due to absence of explicit depth cues in a single RGB image.
PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via Denoised Score Distillation
We first propose a novel score function, Denoised Score Distillation (DSD), which directly modifies the SDS by introducing negative gradient components to iteratively correct the gradient direction and generate high-quality textures.
Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development
Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making.
Implicit Neural Representation for MRI Parallel Imaging Reconstruction
Our approach represents reconstructed fully-sampled images as functions of voxel coordinates and prior feature vectors from undersampled images, addressing the generalization challenges of INR.
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.
V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Models
In this paper, we propose a lightweight solution to this problem by leveraging foundation models, specifically CLIP, CLAP, and AudioLDM.
Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation
The success of automated medical image analysis depends on large-scale and expert-annotated training sets.
Exploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation
We further show that with a simple extension, the generated pseudo sentences can be deployed as weak supervision to boost the 1% semi-supervised image caption benchmark up to 93. 4 CIDEr score (+8. 9) which showcases the versatility and effectiveness of our approach.
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
TractCloud achieves efficient and consistent whole-brain white matter parcellation across the lifespan (from neonates to elderly subjects, including brain tumor patients) without the need for registration.
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project.
Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset
To address this challenge, we propose a post-processing approach that leverages a data-driven method to repair the topology of disconnected pulmonary tubular structures.
SynthMix: Mixing up Aligned Synthesis for Medical Cross-Modality Domain Adaptation
The adversarial methods showed advanced performance by producing synthetic images to mitigate the domain shift, a common problem due to the hardship of acquiring labelled data in medical field.
Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features
In this work, we propose to improve the performance of UDA methods on cross-domain neuron membrane segmentation in EM images.
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
However, due to the insufficient amount of precise annotations, thigh muscle masks generated by deep learning approaches tend to misclassify intra-muscular fat (IMF) as muscle impacting the analysis of muscle volumetrics.
CelebV-Text: A Large-Scale Facial Text-Video Dataset
This paper presents CelebV-Text, a large-scale, diverse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to-video generation tasks.
A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject
Our method is constructed by proposed registration-based peak augmentation (RPA) and uncertainty-based refining (URe) modules.
LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space
In this paper, we propose a novel concept of "anglewise attention" by introducing a multihead self-attention mechanism to the angular domain of an LFI.
Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes
In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features.
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
To solve this problem, we propose a rotation-invariant geometric relation to restore the relative pose with equivariant information for patches defined over different scales.
TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography
This work shows the potential of incorporating anatomical information, especially known anatomical similarities between input features, to guide convolutions in neural networks.
Rethinking Rotation Invariance with Point Cloud Registration
Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned.
TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging
Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly used in neuroscience and neurological clinical research through a Diffusion Tensor Imaging (DTI) model.
PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds
In this paper, we propose a novel framework for 3D neuron reconstruction.
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography
We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography.
Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data
To further facilitate the data efficiency of the cross-domain segmentation methods on the fundus images, we explore UDA optic disc and cup segmentation problems using few labeled source data in this work.
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography.
White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning
In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF).
TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers
Whole brain tractography (WBT) data contains over hundreds of thousands of individual fiber streamlines (estimated brain connections), and this data is usually parcellated to create compact representations for data analysis applications such as disease classification.
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling
In this work, we propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification.
Towards Generalisable Audio Representations for Audio-Visual Navigation
In audio-visual navigation (AVN), an intelligent agent needs to navigate to a constantly sound-making object in complex 3D environments based on its audio and visual perceptions.
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation
In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances.
Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding.
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond
With the backward skip connections, we propose a U-Net based network family, namely Bi-directional O-shape networks, which set new benchmarks on multiple public medical imaging segmentation datasets.
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging
In our previous work, $i. e.$, HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging.
SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning
Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity.
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging the domain distribution gaps.
3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis
General point clouds have been increasingly investigated for different tasks, and recently Transformer-based networks are proposed for point cloud analysis.
Ranked #1 on
3D Point Cloud Classification
on IntrA
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients.
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis
With image-level labels only, patch-wise classification would be sub-optimal due to inconsistency between the patch appearance and image-level label.
Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction
Recently, a series of deep learning based segmentation methods have been proposed to improve the quality of raw 3D optical image stacks by removing noises and restoring neuronal structures from low-contrast background.
Boundary-aware Graph Reasoning for Semantic Segmentation
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.
PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel.
Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation
White matter fiber clustering (WMFC) enables parcellation of white matter tractography for applications such as disease classification and anatomical tract segmentation.
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
We evaluate BiX-NAS on two segmentation tasks using three different medical image datasets, and the experimental results show that our BiX-NAS searched architecture achieves the state-of-the-art performance with significantly lower computational cost.
Network Pruning via Performance Maximization
Specifically, we train a stand-alone neural network to predict sub-networks' performance and then maximize the output of the network as a proxy of accuracy to guide pruning.
Partial Graph Reasoning for Neural Network Regularization
This add-on graph regularizes the network during training and can be completely skipped during inference.
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
Discrete point cloud objects lack sufficient shape descriptors of 3D geometries.
Ranked #10 on
3D Part Segmentation
on ShapeNet-Part
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications
Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis
Scene understanding is a critical problem in computer vision.
Ranked #4 on
3d scene graph generation
on 3DSSG
CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images
Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine.
Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images
One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation.
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images.
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images
In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images.
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
U-Net has become one of the state-of-the-art deep learning-based approaches for modern computer vision tasks such as semantic segmentation, super resolution, image denoising, and inpainting.
ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis
Micro-expressions are reflections of people's true feelings and motives, which attract an increasing number of researchers into the study of automatic facial micro-expression recognition.
Generative Adversarial Network
Micro Expression Recognition
+1
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images.
Shape-Oriented Convolution Neural Network for Point Cloud Analysis
Point cloud is a principal data structure adopted for 3D geometric information encoding.
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images
Specifically, our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch.
Region and Object based Panoptic Image Synthesis through Conditional GANs
Our contribution consists of the proposal of a significant task worth investigating and a naive baseline of solving it.
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion
Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis.
Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
Ranked #1 on
Nuclear Segmentation
on Cell17
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.
Regularized Modal Regression with Applications in Cognitive Impairment Prediction
Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis.
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
The focal loss is further utilized for more effective end-to-end learning.
Locally-Transferred Fisher Vectors for Texture Classification
Texture classification has been extensively studied in computer vision.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments.
Ranked #1 on
Image Clustering
on FRGC
Error Analysis of Generalized Nyström Kernel Regression
Nystr\"{o}m method has been used successfully to improve the computational efficiency of kernel ridge regression (KRR).
Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map.
Robust Saliency Detection via Regularized Random Walks Ranking
In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image.
Fusing Subcategory Probabilities for Texture Classification
Texture, as a fundamental characteristic of objects, has attracted much attention in computer vision research.
