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Found 23 papers, 9 papers with code
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis.
Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification
In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor.
SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance
The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input.
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization
To escape from the dilemma between domain generalization and annotation costs, in this paper, we introduce a novel task named label-efficient domain generalization (LEDG) to enable model generalization with label-limited source domains.
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
(2) A simple and effective spatial-aware inter-scale transformer is designed to interact among consensual regions in multiple scales, which can highlight the cross-scale dependency and resolve the complex scale variations.
CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
Multimodal sentiment analysis and depression estimation are two important research topics that aim to predict human mental states using multimodal data.
Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer
Different from the texture transfer processing of RGB image, we use HR PAN images as the reference images and perform texture transfer for each frequency band of MS images, which is named 2. 5D texture transfer.
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation
To further boost model adaptation performance, we propose a novel method called Attention-based Cross-layer Domain Alignment (ACDA), which captures the semantic relationship between the source and target domains across model layers and calibrates each level of semantic information automatically through a dynamic attention mechanism.
Mixed Transformer U-Net For Medical Image Segmentation
Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlations through Self-Attention (SA).
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
Domain generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains.
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution.
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
In this paper, we investigate a Single Labeled Domain Generalization (SLDG) task with only one source domain being labeled, which is more practical and challenging than the CDG task.
Multi-phase Liver Tumor Segmentation with Spatial Aggregation and Uncertain Region Inpainting
In this work, we propose a novel LiTS method to adequately aggregate multi-phase information and refine uncertain region segmentation.
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome.
Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer
The experiments demonstrated that the prediction accuracy can be improved significantly from 78. 61% (existing radiomics method) and 79. 14% (deep learning method) to 83. 28% by the proposed GGR.
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution
Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples.
Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network.
Cultural Vocal Bursts Intensity Prediction
Recommendation Systems
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data
We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with few parameters.
Interactive Deep Refinement Network for Medical Image Segmentation
Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
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Medical Image Segmentation
on LiTS2017
