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Found 18 papers, 10 papers with code
RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment
Automated chest radiographs interpretation requires both accurate disease classification and detailed radiology report generation, presenting a significant challenge in the clinical workflow.
VerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation
VerSe supports both fully automatic segmentation, through object queries, and interactive mask refinement, by providing click queries when needed.
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification
Although explainability is essential in the clinical diagnosis, most deep learning models still function as black boxes without elucidating their decision-making process.
Implicit In-context Learning
In-context Learning (ICL) empowers large language models (LLMs) to adapt to unseen tasks during inference by prefixing a few demonstration examples prior to test queries.
Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency
The task of shape abstraction with semantic part consistency is challenging due to the complex geometries of natural objects.
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Inspired by the training program of medical radiology residents, we propose a shift towards universal medical image segmentation, a paradigm aiming to build medical image understanding foundation models by leveraging the diversity and commonality across clinical targets, body regions, and imaging modalities.
Visual Prompt Tuning for Test-time Domain Adaptation
First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation.
Ranked #6 on
Domain Adaptation
on VisDA2017
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers
Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis.
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework.
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
Transformers have demonstrated remarkable performance in natural language processing and computer vision.
Modality Bank: Learn multi-modality images across data centers without sharing medical data
Multi-modality images have been widely used and provide comprehensive information for medical image analysis.
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
In this study, we present UTNet, a simple yet powerful hybrid Transformer architecture that integrates self-attention into a convolutional neural network for enhancing medical image segmentation.
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Radiotherapy is a treatment where radiation is used to eliminate cancer cells.
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.
CrossNorm and SelfNorm for Generalization under Distribution Shifts
Can we develop new normalization methods to improve generalization robustness under distribution shifts?
Unity of Opposites: SelfNorm and CrossNorm for Model Robustness
CrossNorm exchanges styles between feature channels to perform style augmentation, diversifying the content and style mixtures.
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
First is that most if not all modern augmentation search methods are offline and learning policies are isolated from their usage.
FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images
In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images.
