Lung Nodule Classification
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Benchmarks
LIDC-IDRI
Most implemented
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Papers
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+2