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Found 13 papers, 10 papers with code
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
MSA$^2$Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation
Medical image segmentation involves identifying and separating object instances in a medical image to delineate various tissues and structures, a task complicated by the significant variations in size, shape, and density of these features.
Physics-Inspired Generative Models in Medical Imaging: A Review
Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging.
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis
State Space Models (SSMs), specifically the \textit{\textbf{Mamba}} model with selection mechanisms and hardware-aware architecture, have garnered immense interest lately in sequential modeling and visual representation learning, challenging the dominance of transformers by providing infinite context lengths and offering substantial efficiency maintaining linear complexity in the input sequence.
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
The rise of Transformer architectures has revolutionized medical image segmentation, leading to hybrid models that combine Convolutional Neural Networks (CNNs) and Transformers for enhanced accuracy.
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks.
Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects
Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning algorithms.
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine.
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models.
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components.
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions.
Ranked #1 on
Lesion Segmentation
on HAM10000
Medical Image Segmentation Review: The success of U-Net
U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities.
Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images
On top of the cytoplasm segmentation network, we included a scale aggregation function to refine and generate a final prediction.
