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Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/mp.13764openalex-snapshot
Computer‐aided diagnosis in the era of deep learning
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Robustness Analysis of Eleven Linear Classifiers in Extremely High–Dimensional Feature Spaces
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PARAMETER ESTIMATION
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A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
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Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space
inferredcites →https://doi.org/10.1088/1361-6560/aa93d4openalex-snapshot
Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms
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Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis
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Prediction error estimation: a comparison of resampling methods
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Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
inferredcites →https://doi.org/10.1109/jbhi.2016.2635663openalex-snapshot
An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification
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Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets
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Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks
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Quality assurance and training procedures for computer‐aided detection and diagnosis systems in clinical usea)
inferredcites →https://doi.org/10.1118/1.4816310openalex-snapshot
Evaluation of computer-aided detection and diagnosis systemsa)
inferredcites →https://doi.org/10.1118/1.4967345openalex-snapshot
Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography
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Classifier design for computer‐aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers
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ImageNet classification with deep convolutional neural networks
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CAD and AI for breast cancer—recent development and challenges
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Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
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Advanced deep learning architectures for enhanced mammography classification: a comparative study of CNNs and ViT
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Deep Learning Cascaded Feature Selection Framework for Breast Cancer Classification: Hybrid CNN with Univariate-Based Approach
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Applications of machine learning and multimodal integration for the early diagnosis of neurodegenerative diseases (Review)
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Additional file 1 of Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison
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Science and Advanced Computing — runs a structured editorial panel for researchers
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