Papers Lung Nodule Classification
“Lung Nodule Classification” 태그가 달린 논문 40편 · 필터 해제
Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random masking methods make SSL less effective…
Lung Nodule ClassificationSelf-Supervised LearningTumor SegmentationVIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs
Vision-language pretraining has driven significant progress in medical image analysis. However, current methods typically supervise visual encoders using one-hot labels or free-form text, neither of which effectively cap…
Lung Nodule ClassificationStructured PredictionLung nodule classification on CT scan patches using 3D convolutional neural networks
Lung cancer remains one of the most common and deadliest forms of cancer worldwide. The likelihood of successful treatment depends strongly on the stage at which the disease is diagnosed. Therefore, early detection of lu…
Lung Nodule ClassificationLung Nodule DetectionMinimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual att…
Lung Nodule ClassificationMulti-Attention Stacked Ensemble for Lung Cancer Detection in CT Scans
In this work, we address the challenge of binary lung nodule classification (benign vs malignant) using CT images by proposing a multi-level attention stacked ensemble of deep neural networks. Three pretrained backbones …
Lung Nodule ClassificationMedical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DINOv2 are encouraging …
ClassificationDiagnosticExplainable artificial intelligenceExplainable Models+2Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier
In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our approach integrates advanced image standardi…
ClassificationDecision MakingLung Nodule ClassificationVariational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Lung cancer is responsible for 21% of cancer deaths in the UK and five-year survival rates are heavily influenced by the stage the cancer was identified at. Recent studies have demonstrated the capability of AI methods f…
Lung Cancer DiagnosisLung Nodule ClassificationLung cancer detection from thoracic CT scans using an ensemble of deep learning models
Lung cancer remains a prevalent and deadly disease, claiming numerous lives annually. Early detection plays a pivotal role in significantly improving survival rates, by up to 50–70%. Therefore, developing a robust lung c…
Computed Tomography (CT)DiagnosticEnsemble LearningLung Nodule Classification+1Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?
Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. U…
Deep LearningLung Nodule ClassificationWavelet leader based formalism to compute multifractal features for classifying lung nodules in X-ray images
This paper presents and validates a novel lung nodule classification algorithm that uses multifractal features found in X-ray images. The proposed method includes a pre-processing step where two enhancement techniques ar…
Data AugmentationLung Nodule ClassificationDeep fusion of gray level co-occurrence matrices for lung nodule classification
Lung cancer is a severe menace to human health, due to which millions of people die because of late diagnoses of cancer; thus, it is vital to detect the disease as early as possible. The Computerized chest analysis Tomog…
Lung Nodule ClassificationSpecificityFaithful learning with sure data for lung nodule diagnosis
Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice…
ClassificationLung Nodule ClassificationRetrievalThe Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results. In lung nodule classification, for e…
Lung Nodule ClassificationThe Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging. While most studies deploy cross-entropy as the loss function in such tasks, a growing numbe…
ClassificationContrastive Learningimage-classificationImage Classification+2Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics
Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer computer aided diagnosis (CAD) can play a crucial role. However, most published CAD methods treat lung cancer diagnosis as a lung nodu…
Deep AttentionLung Cancer DiagnosisLung Nodule ClassificationMultiple Instance LearningMeta ordinal weighting net for improving lung nodule classification
The progression of lung cancer implies the intrinsic ordinal relationship of lung nodules at different stages-from benign to unsure then to malignant. This problem can be solved by ordinal regression methods, which is be…
ClassificationGeneral ClassificationLung Nodule ClassificationMeta-Learning+1Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification
Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically o…
General ClassificationLung Nodule ClassificationNeural Architecture SearchPulmonary Nodules Classification3D Axial-Attention for Lung Nodule Classification
Purpose: In recent years, Non-Local based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to low-resolution feature maps. Moreover, …
ClassificationGeneral ClassificationLung Nodule ClassificationMeta Ordinal Regression Forest For Learning with Unsure Lung Nodules
Deep learning-based methods have achieved promising performance in early detection and classification of lung nodules, most of which discard unsure nodules and simply deal with a binary classification -- malignant vs ben…
Binary ClassificationClassificationfeature selectionGeneral Classification+3