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논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation

Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework

Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning

Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification

Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers

Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation

Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics

ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation

Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging

FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation

An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation

Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework

Subgroup Performance Analysis in Hidden Stratifications

ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation

Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions

Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification

GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis

Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification

Image Segmentation: Inducing graph-based learning

MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation

QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation

Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions

A Cascaded Dilated Convolution Approach for Mpox Lesion Classification

Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

Enhancing the Generalization Capability of Skin Lesion Classification Models with Active Domain Adaptation Methods

TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation

Evidential Federated Learning for Skin Lesion Image Classification

When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation

A Multimodal Vision Foundation Model for Clinical Dermatology

LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection

Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization

A Novel Perspective for Multi-modal Multi-label Skin Lesion Classification

SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance

SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation

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