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Lung Cancer Diagnosis
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images
Explainability Through Human-Centric Design for XAI in Lung Cancer Detection
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Early Lung Cancer Detection
AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction
Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining
Tool or Tutor? Experimental evidence from AI deployment in cancer diagnosis
Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning
Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images
Evaluation of radiomic feature harmonization techniques for benign and malignant pulmonary nodules
Refining Focus in AI for Lung Cancer: Comparing Lesion-Centric and Chest-Region Models with Performance Insights from Internal and External Validation
Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey
MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule
Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation
Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data
Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging
Snuffy: Efficient Whole Slide Image Classifier
A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset
No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases
AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
Lung Cancer Detection Using Machine Learning Methods
Automated Report Generation for Lung Cytological Images Using a CNN Vision Classifier and Multiple-Transformer Text Decoders: Preliminary Study
NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction
Health-related Quality of life, Financial Toxicity, Productivity Loss and Catastrophic Health Expenditures After Lung Cancer Diagnosis in Argentina
Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Exploring Counterfactual Alignment Loss towards Human-centered AI
Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review
User lung cancer classification using efficientnet from ct scan images
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules
LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model
MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan
Specialty-Oriented Generalist Medical AI for Chest CT Screening
DiffusionCT: Latent Diffusion Model for CT Image Standardization
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
Enhancing Early Lung Cancer Detection on Chest Radiographs with AI-assistance: A Multi-Reader Study
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.