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Found 44 papers, 25 papers with code
MedTet: An Online Motion Model for 4D Heart Reconstruction
We present a novel approach to reconstruction of 3D cardiac motion from sparse intraoperative data.
Fundus to Fluorescein Angiography Video Generation as a Retinal Generative Foundation Model
Fundus fluorescein angiography (FFA) is crucial for diagnosing and monitoring retinal vascular issues but is limited by its invasive nature and restricted accessibility compared to color fundus (CF) imaging.
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss.
Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance
In this work, we pioneer dynamic FFA video generation from static CF images.
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging
EyeFound provides a generalizable solution to improve model performance and lessen the annotation burden on experts, facilitating widespread clinical AI applications for retinal imaging.
Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structures
Accurately segmenting 3D curvilinear structures in medical imaging remains challenging due to their complex geometry and the scarcity of diverse, large-scale datasets for algorithm development and evaluation.
DataCook: Crafting Anti-Adversarial Examples for Healthcare Data Copyright Protection
This paper introduces a novel approach, named DataCook, designed to safeguard the copyright of healthcare data during the deployment phase.
LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models
Previous efforts in medical imaging synthesis have struggled with separating lesion information from background, resulting in low-quality backgrounds and limited control over the synthetic output.
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields
Our method addresses these issues by shifting from dense voxel to sparse point representation, offering better memory efficiency and global context utilization.
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.
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods.
Enforcing 3D Topological Constraints in Composite Objects via Implicit Functions
Medical applications often require accurate 3D representations of complex organs with multiple parts, such as the heart and spine.
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.
The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases
This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations
In this paper, we focus on improving ViT-AE (nicknamed ViT-AE++) for a more effective representation of 2D and 3D medical images.
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction.
LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection
Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection.
What Makes for Automatic Reconstruction of Pulmonary Segments
The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing.
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts.
Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision
Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation.
Representation-Agnostic Shape Fields
In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning.
ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging
Deep implicit shape models have become popular in the computer vision community at large but less so for biomedical applications.
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes.
Asymmetric 3D Context Fusion for Universal Lesion Detection
The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion.
Weakly Supervised Volumetric Image Segmentation with Deformed Templates
There are many approaches to weakly-supervised training of networks to segment 2D images.
3D Human Action Representation Learning via Cross-View Consistency Pursuit
In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary supervision signal.
Shape Self-Correction for Unsupervised Point Cloud Understanding
Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal.
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
We present MedMNIST, a collection of 10 pre-processed medical open datasets.
Learning Black-Box Attackers with Transferable Priors and Query Feedback
This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available.
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
A three-level hierarchical classification system for pulmonary lesions is developed, which covers most diseases in cancer-related diagnosis.
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy.
Ranked #1 on
Text-To-Speech Synthesis
on 20000 utterances
(using extra training data)
Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules
It is achieved by predicting future displacement field of each voxel with a WarpNet.
AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness.
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules.
Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients.
Reinventing 2D Convolutions for 3D Images
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.
Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis
The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable.
Evaluating and Boosting Uncertainty Quantification in Classification
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making.
Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling
Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points.
Adversarial Attack and Defense on Point Sets
A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment.
