Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization
In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model.
GAUSSIAN PROCESSES HYPERPARAMETER OPTIMIZATION LUNG NODULE CLASSIFICATION
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Ranked #4 on
Lung Nodule Classification
on LIDC-IDRI
Fast CapsNet for Lung Cancer Screening
We show that CapsNets significantly outperforms CNNs when the number of training samples is small.
COMPUTED TOMOGRAPHY (CT) IMAGE RECONSTRUCTION LUNG NODULE CLASSIFICATION
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
Lung cancer is the leading cause of cancer-related death worldwide.
COMPUTED TOMOGRAPHY (CT) LUNG NODULE CLASSIFICATION TRANSFER LEARNING
Lung Nodule Classification using Deep Local-Global Networks
In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
Ranked #5 on
Lung Nodule Classification
on LIDC-IDRI
COMPUTED TOMOGRAPHY (CT) LUNG NODULE CLASSIFICATION TRANSFER LEARNING
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification
• fei-hdu/NAS-Lung
• 
Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis.
Ranked #1 on
Neural Architecture Search
on LIDC-IDRI
LUNG NODULE CLASSIFICATION NEURAL ARCHITECTURE SEARCH PULMONARY NODULES CLASSIFICATION