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Found 36 papers, 24 papers with code
Endora: Video Generation Models as Endoscopy Simulators
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.
UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
Moreover, to unleash the generalization capability of SAM across a variety of nuclei images, we devise a Domain-adaptive Tuning Encoder (DT-Encoder) to seamlessly harmonize visual features with domain-common and domain-specific knowledge, and further devise a Domain Query-enhanced Decoder (DQ-Decoder) by leveraging learnable domain queries for segmentation decoding in different nuclei domains.
EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction
To adapt 3DGS for endoscopic scenes, we propose two strategies, Holistic Gaussian Initialization (HGI) and Spatio-temporal Gaussian Tracking (SGT), to handle the non-trivial Gaussian initialization and tissue deformation problems, respectively.
Fine-Grained Image-Text Alignment in Medical Imaging Enables Cyclic Image-Report Generation
To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process.
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM).
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation
Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Artificial General Intelligence for Medical Imaging
In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
Adjustment and Alignment for Unbiased Open Set Domain Adaptation
In a nutshell, ANNA consists of Front-Door Adjustment (FDA) to correct the biased learning in the source domain and Decoupled Causal Alignment (DCA) to transfer the model unbiasedly.
Novel Scenes & Classes: Towards Adaptive Open-set Object Detection
Then, a high-order metric is proposed to match the most significant motif as high-order patterns, serving for motif-guided novel-class learning.
MRM: Masked Relation Modeling for Medical Image Pre-Training with Genetics
Moreover, to enhance semantic relation modeling, we propose relation matching to align the sample-wise relation between the intact and masked features.
FedPD: Federated Open Set Recognition with Parameter Disentanglement
To this end, we aim to study a novel problem of federated open-set recognition (FedOSR), which learns an open-set recognition (OSR) model under federated paradigm such that it classifies seen classes while at the same time detects unknown classes.
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks
Specifically, we extend three distinct adversarial attacks to the 3D object detection task, benchmarking the robustness of state-of-the-art LiDAR-based 3D object detectors against attacks on the KITTI and Waymo datasets.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
A heterogeneous group CNN for image super-resolution
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty.
Ranked #1 on
3D Object Detection
on KITTI Cars Easy
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels
Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations.
Image Super-resolution with An Enhanced Group Convolutional Neural Network
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR).
Towards Robust Adaptive Object Detection under Noisy Annotations
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images.
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation
By exploiting computational geometry analysis and properties of segmentation, we design three complementary regularizers, i. e. volume regularization, anchor guidance, convex guarantee, to approximate the true SimT.
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the adaptation with graph matching.
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis
Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other.
Exploring Gradient Flow Based Saliency for DNN Model Compression
Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead.
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
With the contribution of the CCD and CRP, our CRCKD algorithm can distill the relational knowledge more comprehensively.
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation
Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation.
Ranked #30 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas.
