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Found 8 papers, 2 papers with code
3D Axial-Attention for Lung Nodule Classification
Methods: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i. e., self-attention).
A new semi-supervised self-training method for lung cancer prediction
This study presents a complete end-to-end scheme to detect and classify lung nodules using the state-of-the-art Self-training with Noisy Student method on a comprehensive CT lung screening dataset of around 4, 000 CT scans.
ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification
Nevertheless, the large variation in the size and heterogeneous appearance of the nodules makes this task an extremely challenging one.
Ranked #2 on
Lung Nodule Classification
on LIDC-IDRI
Cribriform pattern detection in prostate histopathological images using deep learning models
In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
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 #6 on
Lung Nodule Classification
on LIDC-IDRI
Gated-Dilated Networks for Lung Nodule Classification in CT scans
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans.
Ranked #3 on
Lung Nodule Classification
on LIDC-IDRI
Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks
Besides, these images are not safe for work (NSFW) in which employees should not be seen accessing such contents during work.
Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator
In this paper, both Dense SIFT and regular SIFT are studied and compared when merged with CNN features.
