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Found 51 papers, 32 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.
Single-Layer Learnable Activation for Implicit Neural Representation (SL$^{2}$A-INR)
We have identified that these problems can be greatly alleviated by introducing a paradigm shift in INRs.
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.
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.
Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems.
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training.
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.
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use.
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function.
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.
Self-supervised Semantic Segmentation: Consistency over Transformation
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks.
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
Implicit Neural Representation in Medical Imaging: A Comparative Survey
Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging.
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach
This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data.
Ranked #17 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
(3) In contrast to a bridge that only contains token-wise self-attention, we propose a Dual Transformer Bridge that also includes channel-wise self-attention to exploit correlations between scales at different stages from a dual perspective.
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision.
DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies.
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.
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms.
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism.
TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation
Especially, deep neural networks based on seminal architectures such as U-shaped models with skip-connections or atrous convolution with pyramid pooling have been tailored to a wide range of medical image analysis tasks.
TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model
In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era.
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation.
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells.
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation.
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging.
Contextual Attention Network: Transformer Meets U-Net
Then, they emphasize the informative regions while taking into account the long-range contextual dependency derived by the Transformer module.
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling
To further improve the performance of the proposed method, we propose a skeleton-based search space to reduce false positive detection.
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.
Semi-supervised few-shot learning for medical image segmentation
To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning.
Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation
These blocks adaptively recalibrate the channel-wise feature responses by utilizing a self-gating mechanism of the global information embedding of the feature maps.
Ranked #9 on
Lesion Segmentation
on ISIC 2018
On the Texture Bias for Few-Shot CNN Segmentation
Despite the initial belief that Convolutional Neural Networks (CNNs) are driven by shapes to perform visual recognition tasks, recent evidence suggests that texture bias in CNNs provides higher performing models when learning on large labeled training datasets.
Ranked #2 on
Few-Shot Semantic Segmentation
on Pascal5i
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
Ranked #1 on
Lung Nodule Segmentation
on Lung Nodule
Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
In feature extraction stage the Cross-correlation coefficient is applied on the gesture to recognize it.
Real-Time Human-Computer Interaction Based on Face and Hand Gesture Recognition
In the proposed technique, first, the hand gesture and face location is extracted from the main image by combination of skin and cascade detector and then is sent to recognition stage.
Novel and Fast Algorithm for Extracting License Plate Location Based on Edge Analysis
Nowadays in developing or developed countries, the Intelligent Transportation System (ITS) technology has attracted so much attention to itself.
Novel and Tuneable Method for Skin Detection Based on Hybrid Color Space and Color Statistical Features
In the first one, from pure skin statistical features were extracted and at the second stage, the skin pixels are detected using HSV and YCbCr color spaces.
Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System
This method has been tested on available data set that has different images of the background, considering distance, and angel of view so that the correct extraction rate of plate reached at 98% and character recognition rate achieved at 99. 12%.
Recognition of Handwritten Persian/Arabic Numerals Based on Robust Feature Set and K-NN Classifier
This paper has been withdrawn by the author due to a crucial sign error in equation 2 and some mistake in Table 1 information.
A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition
License Plate Recognition (LPR) plays an important role on the traffic monitoring and parking management.
New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component
License Plate recognition plays an important role on the traffic monitoring and parking management systems.
A robust and adaptable method for face detection based on Color Probabilistic Estimation Technique
In the first one, the skin intensity distribution is estimated using some train photos of pure skin, and at the second stage, the skin pixels are detected using Gaussian model and optimal threshold tuning.
Novel and Automatic Parking Inventory System Based on Pattern Recognition and Directional Chain Code
The system is then allowed to open parking barrier for the vehicle and generate entrance cost receipt.
Optimized Method for Iranian Road Signs Detection and recognition system
Road sign recognition is one of the core technologies in Intelligent Transport Systems.
Classifiers fusion method to recognize handwritten persian numerals
For evaluation of the proposed method we considered a Persian numerals database with 20, 000 handwritten samples.
