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Found 30 papers, 12 papers with code
CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole.
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.
Learning from partially labeled data for multi-organ and tumor segmentation
To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets.
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation.
Inter-slice Context Residual Learning for 3D Medical Image Segmentation
In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images.
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification
Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier
In this paper, we advocate bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS.
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data.
Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation
In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain.
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.
An Attention-Gated Convolutional Neural Network for Sentence Classification
The classification of sentences is very challenging, since sentences contain the limited contextual information.
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding
Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes.
A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images
A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images.
Natural-Logarithm-Rectified Activation Function in Convolutional Neural Networks
Activation functions play a key role in providing remarkable performance in deep neural networks, and the rectified linear unit (ReLU) is one of the most widely used activation functions.
A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification
In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e. g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function.
Pairwise Relation Learning for Semi-supervised Gland Segmentation
In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation
In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space.
Kernel Adversarial Learning for Real-world Image Super-resolution
Current deep image super-resolution (SR) approaches attempt to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises.
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process.
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers
Besides, we further extend the clustering-guided attention from single-scale to multi-scale, which is conducive to dense prediction tasks.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
Attention Mechanisms in Medical Image Segmentation: A Survey
This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation.
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations.
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
In this paper, we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging.
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios
In this paper, we extend the scope of Med-VLP to encompass 3D images, specifically targeting full-body scenarios, by using a multimodal dataset of CT images and reports.
