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Found 58 papers, 37 papers with code
GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation
Additionally, a Multi-view Local-based Fusion Module (MLFM) is designed to extract correlations of cardiac structures from different views.
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains.
HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving
For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple autonomous driving tasks from videos, i. e., the Risk Object Localization and Intention and Suggestion Prediction (ROLISP) task.
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
Subsequently, we introduce two loss functions: the image-text binary-opposite loss and the text semantic-opposite loss, which we use to teach CLIPN to associate images with no prompts, thereby enabling it to identify unknown samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
To this end, we propose a context-aware pseudo-label refinement method for SF-UDA.
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss.
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
Our framework enhances the OCT model during training by utilizing unpaired fundus images and does not require the use of fundus images during testing, which greatly improves the practicality and efficiency of our method for clinical use.
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners.
FSDiffReg: Feature-wise and Score-wise Diffusion-guided Unsupervised Deformable Image Registration for Cardiac Images
Specifically, FDG uses the diffusion model's multi-scale semantic features to guide the generation of the deformation field.
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required.
DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D medical image segmentation.
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition
Through these modules, FoPro-KD achieves significant improvements in performance on long-tailed medical image classification benchmarks, demonstrating the potential of leveraging the learned frequency patterns from pre-trained models to enhance transfer learning and compression of large pre-trained models for feasible deployment.
Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions.
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
This study investigates barely-supervised medical image segmentation where only few labeled data, i. e., single-digit cases are available.
CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks
Contrastive Language-Image Pre-training (CLIP) is a powerful multimodal large vision model that has demonstrated significant benefits for downstream tasks, including many zero-shot learning and text-guided vision tasks.
Ranked #1 on
Open Vocabulary Semantic Segmentation
on COCO-Stuff-171
(mIoU metric)
Interactive Segmentation
Open Vocabulary Semantic Segmentation
+2
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval
Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness.
Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
In this paper, we propose a novel framework, TransPro, that translates 3D Optical Coherence Tomography (OCT) images into exclusive 3D OCTA images using an image translation pattern.
Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed.
Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks
In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression.
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images.
Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos
We also introduce teacher-student distillation to distill the information from LV segmentation masks into an end-to-end LVEF regression model that only requires video inputs.
TripleE: Easy Domain Generalization via Episodic Replay
In this paper, we propose TripleE, and the main idea is to encourage the network to focus on training on subsets (learning with replay) and enlarge the data space in learning on subsets.
Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels
Our main goal is to improve the quality of pseudo labels under extreme MRI Analysis with various domains.
FreeSeg: Free Mask from Interpretable Contrastive Language-Image Pretraining for Semantic Segmentation
We call the proposed framework as FreeSeg, where the mask is freely available from raw feature map of pretraining model.
Exploring Visual Interpretability for Contrastive Language-Image Pre-training
For this problem, we propose the Explainable Contrastive Language-Image Pre-training (ECLIP), which corrects the explainability via the Masked Max Pooling.
Learning Shadow Correspondence for Video Shadow Detection
We further design a new evaluation metric to evaluate the temporal stability of the video shadow detection results.
Sub-cluster-aware Network for Few-shot Skin Disease Classification
Specifically, SCAN follows a dual-branch framework, where the first branch is to learn class-wise features to distinguish different skin diseases, and the second one aims to learn features which can effectively partition each class into several groups so as to preserve the sub-clustered structure within each class.
Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images
Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions
Our key insight is to distill knowledge from publicly available models trained on large generic datasets4 to facilitate the self-supervised learning of surgical videos.
Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation
Our framework leverages label distribution to encourage the network to put more effort into learning cartilage parts.
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval
On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients.
Less is More: Surgical Phase Recognition from Timestamp Supervision
Our study uncovers unique insights of surgical phase recognition with timestamp supervisions: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation cost; 3) less is more in surgical phase recognition, i. e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug and play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods.
Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation
With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels.
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
So we try to improve WSSS in the aspect of noise mitigation.
Ranked #13 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Saliency Detection
Weakly supervised Semantic Segmentation
+1
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information.
Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos
Automatic surgical phase recognition plays a vital role in robot-assisted surgeries.
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.
Improved Heatmap-based Landmark Detection
This paper proposes a landmark detection network for detecting sutures in endoscopic pictures, which solves the problem of a variable number of suture points in the images.
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images.
Support-Set Based Cross-Supervision for Video Grounding
The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.
Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation
To deal with the high inter-rater variability, the study of imperfect label has great significance in medical image segmentation tasks.
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance.
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making.
Effects of Regional Trade Agreement to Local and Global Trade Purity Relationships
In addition, we apply the network to reflect the purity of the trade relations among countries.
The 'Letter' Distribution in the Chinese Language
In addition, we collected Chinese literature corpora for different historical periods from the Tang Dynasty to the present, and we dismantled the Chinese written language into three kinds of basic particles: characters, strokes and constructive parts.
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.
CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision.
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.
Revisiting Metric Learning for Few-Shot Image Classification
However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.
Difficulty-aware Meta-learning for Rare Disease Diagnosis
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data.
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model
In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images
Our best model achieves 77. 23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Ranked #1 on
Liver Segmentation
on LiTS2017
(Dice metric)
Automatic Liver And Tumor Segmentation
Image Segmentation
+3
