Papers Medical Image Classification
“Medical Image Classification” 태그가 달린 논문 551편 · 필터 해제
MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
Reliable evaluation of vision-language models (VLMs) and medical vision-language models (Medical-VLMs) requires calibrated confidence, particularly under realistic clinical conditions. However, existing efforts mainly fo…
Medical Image ClassificationLabel-Free Foundational Model Selection for Medical Image Classification under Distribution Shift via Pseudo Label Discrepancy
Foundation models are increasingly deployed for medical image analysis. However, under the inter-institutional distribution shift typical of deployment, their performance varies widely and cannot be known without target-…
Medical Image ClassificationHow Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification
Active learning (AL) promises to reduce the cost of medical imaging projects by lowering the number of clinical labels required. However, practical deployment requires committing to a sampling strategy before the full an…
Medical Image ClassificationActive LearningRecurrent Contrastive Learning for Imbalanced Medical Image Classification
Medical image classification often suffers from class imbalance due to the inherent disparities in disease incidence. Existing approaches, such as class resampling and loss reweighting, mainly improve learning within the…
Medical Image ClassificationContrastive LearningWhat Makes Deep Learning Work for Traditional Chinese Medicine Tongue Diagnosis? A Comprehensive Ablation Study
Deep learning has shown promise for automated tongue diagnosis in traditional Chinese medicine (TCM), yet the design space remains underexplored. We conducted a systematic ablation study spanning 20+ model versions under…
Medical Image ClassificationScaFE: Data-Efficient Scar Classification with LLM-Generated Clinical Feature Programs
Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and substantial acquisition variation across hospitals. End-to-end imag…
Medical Image ClassificationFeature EngineeringClinical KnowledgeSteering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approac…
Medical Image ClassificationReinforcement LearningDomain AdaptationLearning To Focus: Anatomy-Guided Attention Regularization for Medical Image Classification
Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classification losses rarely provide spatial supervision. Explicit supervision v…
Medical Image ClassificationSecure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling
Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development. Image disguising has recently emerged as a …
Medical Image ClassificationSemantic SegmentationMSA-DCNN: A Data-Efficient Multi-Scale Attention Deformable CNN for Medical Image Classification
Existing deep learning methods perform well in medical image classification but struggle with multi-scale morphology and limited annotations due to fixed sampling and data-hungry training. Existing approaches address the…
Medical Image ClassificationProbabilistic Robustness in Medical Image Classification
Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical clinical settings, where prediction errors under perturbations may le…
Medical Image ClassificationAdversarial RobustnessCPR: Chained Perceptual Refinement for Coarse-to-Fine Medical Image Classification
High resolution medical images contain fine grained, spatially sparse cues that are critical for diagnosis, yet preserving full resolution incurs substantial computational and memory costs. Most deep models process image…
Medical Image ClassificationParameter-Efficient Continuous-Variable Photonic Quantum Neural Networks for Edge Quantum AI: Demonstration in Oral Cancer Detection
Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings. Smartphone-based screening is a scalable alternative but needs lightweight mode…
Medical Image ClassificationQuantum Machine LearningMedDiffuseMix: Preserving Diagnostic Evidence with Saliency-Aware Diffusion Medical Image Data Augmentatio
Limited data availability, class imbalance, and domain variability remain major barriers to reliable medical image classification. Conventional augmentation can improve training diversity but may distort diagnostically i…
Medical Image ClassificationImage AugmentationOne-Shot Data Selection for Medical Image Classification via Graph Coverage
Training medical image classifiers on entire datasets is wasteful when annotation budgets are limited: not all samples contribute equally, yet acquiring expert labels is expensive. Active learning reduces annotation cost…
Medical Image ClassificationActive LearningOTCHA: Optimal Transport-driven Confidence-aware Latent Hub Alignment for Multi-View Medical Image Classification
Multi-view imaging, such as mammography and chest radiography, is a standard component of clinical practice. However, medical images are often unregistered and contain view-specific artifacts or irrelevant background cue…
Medical Image ClassificationA Controlled Benchmark of Quantum-Latent GAN Augmentation for Brain MRI
Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains.…
Medical Image ClassificationWhen LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, …
Medical Image ClassificationFeature EngineeringInput-Dependent Fisher Information for Local Sensitivity Analysis of Medical Image Classifiers
Deep neural networks have achieved strong performance in medical image classification, but often work like black-box. Commonly used post-hoc interpretation methods often provide heuristic visualizations whose relationshi…
Medical Image ClassificationLLM-Guided Evolution for Medical Decision Pipelines
Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-guided MAP-Elites evolution as an inference-time alternative for disco…
Medical Image Classification