Papers Medical Image Analysis
“Medical Image Analysis” 태그가 달린 논문 1,360편 · 필터 해제
MUPAX: Multidimensional Problem Agnostic eXplainable AI
Robust XAI techniques should ideally be simultaneously deterministic, model agnostic, and guaranteed to converge. We propose MULTIDIMENSIONAL PROBLEM AGNOSTIC EXPLAINABLE AI (MUPAX), a deterministic, model agnostic expla…
Anatomical Landmark DetectionAudio ClassificationBenchmarkingFeature Importance+3MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification
Current medical image analysis systems are typically task-specific, requiring separate models for classification and segmentation, and lack the flexibility to support user-defined workflows. To address these challenges, …
Image SegmentationLarge Language ModelMedical Image AnalysisMedical Image Segmentation+2U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of …
Edge DetectionMedical Image AnalysisSemantic SegmentationA Deep Learning Based Method for Fast Registration of Cardiac Magnetic Resonance Images
Image registration is used in many medical image analysis applications, such as tracking the motion of tissue in cardiac images, where cardiac kinematics can be an indicator of tissue health. Registration is a challengin…
Image RegistrationMedical Image AnalysisTaming Vision-Language Models for Medical Image Analysis: A Comprehensive Review
Modern Vision-Language Models (VLMs) exhibit unprecedented capabilities in cross-modal semantic understanding between visual and textual modalities. Given the intrinsic need for multi-modal integration in clinical applic…
Medical Image AnalysisPrompt LearningMultimodal Medical Image Binding via Shared Text Embeddings
Medical image analysis increasingly relies on the integration of multiple imaging modalities to capture complementary anatomical and functional information, enabling more accurate diagnosis and treatment planning. Achiev…
Cross-Modal RetrievalMedical Image AnalysisFedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation
Federated learning (FL) has emerged as a promising approach for collaborative medical image analysis, enabling multiple institutions to build robust predictive models while preserving sensitive patient data. In the conte…
ClassificationDataset DistillationFederated Learningimage-classification+2Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature e…
Computational EfficiencyDiagnosticMedical Image AnalysisStroke ClassificationConquering the Retina: Bringing Visual in-Context Learning to OCT
Recent advancements in medical image analysis have led to the development of highly specialized models tailored to specific clinical tasks. These models have demonstrated exceptional performance and remain a crucial rese…
In-Context LearningMedical Image AnalysisBRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In …
Brain Tumor SegmentationMedical Image AnalysisSegmentationTumor Segmentation3D Skin Segmentation Methods in Medical Imaging: A Comparison
Automatic segmentation of anatomical structures is critical in medical image analysis, aiding diagnostics and treatment planning. Skin segmentation plays a key role in registering and visualising multimodal imaging data.…
Medical Image AnalysisSegmentationAutonomous Computer Vision Development with Agentic AI
Agentic Artificial Intelligence (AI) systems leveraging Large Language Models (LLMs) exhibit significant potential for complex reasoning, planning, and tool utilization. We demonstrate that a specialized computer vision …
Medical Image AnalysisSSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation
In the era of information explosion, efficiently leveraging large-scale unlabeled data while minimizing the reliance on high-quality pixel-level annotations remains a critical challenge in the field of medical imaging. S…
Data AugmentationImage SegmentationMedical Image AnalysisMedical Image Segmentation+3Multi-modal brain MRI synthesis based on SwinUNETR
Multi-modal brain magnetic resonance imaging (MRI) plays a crucial role in clinical diagnostics by providing complementary information across different imaging modalities. However, a common challenge in clinical practice…
DiagnosticMedical Image AnalysisDrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?
Vision-language models (VLMs) exhibit strong zero-shot generalization on natural images and show early promise in interpretable medical image analysis. However, existing benchmarks do not systematically evaluate whether …
DiagnosticMedical Image AnalysisVisual ReasoningZero-shot GeneralizationCan Large Language Models Challenge CNNs in Medical Image Analysis?
This study presents a multimodal AI framework designed for precisely classifying medical diagnostic images. Utilizing publicly available datasets, the proposed system compares the strengths of convolutional neural networ…
DiagnosticMedical Image AnalysisTowards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions
In recent years, Fine-Grained Visual Classification (FGVC) has achieved impressive recognition accuracy, despite minimal inter-class variations. However, existing methods heavily rely on instance-level labels, making the…
ClassificationDictionary LearningFine-Grained Image ClassificationMedical Image Analysis+1Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging
In medical image analysis, feature engineering plays an important role in the design and performance of machine learning models. Persistent homology (PH), from the field of topological data analysis (TDA), demonstrates r…
Feature EngineeringMedical Image AnalysisTopological Data AnalysisOptimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off
The rapid advancement of deep learning in medical image analysis has greatly enhanced the accuracy of skin cancer classification. However, current state-of-the-art models, especially those based on transfer learning like…
Cancer ClassificationMedical Image AnalysisSkin Cancer ClassificationTransfer LearningRep3D: Re-parameterize Large 3D Kernels with Low-Rank Receptive Modeling for Medical Imaging
In contrast to vision transformers, which model long-range dependencies through global self-attention, large kernel convolutions provide a more efficient and scalable alternative, particularly in high-resolution 3D volum…
Inductive BiasMedical Image Analysis