On the public citation graph

SANet: A Slice-Aware Network for Pulmonary Nodule Detection

https://doi.org/10.1109/tpami.2021.3065086

0 signed · 190 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jama.2016.17216openalex-snapshot

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

inferredcites →https://doi.org/10.1002/ijc.29210openalex-snapshot

Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012

inferredcites →https://doi.org/10.1007/978-3-030-01261-8_1openalex-snapshot

Group Normalization

inferredcites →https://doi.org/10.1007/978-3-030-32226-7_30openalex-snapshot

NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation

inferredcites →https://doi.org/10.1007/978-3-030-59725-2_53openalex-snapshot

CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans

inferredcites →https://doi.org/10.1007/978-3-319-10470-6_58openalex-snapshot

Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning

inferredcites →https://doi.org/10.1007/978-3-319-14612-6_35openalex-snapshot

A Technique for Lung Nodule Candidate Detection in CT Using Global Minimization Methods

inferredcites →https://doi.org/10.1007/978-3-319-19992-4_46openalex-snapshot

Multi-scale Convolutional Neural Networks for Lung Nodule Classification

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_45openalex-snapshot

Multi-region Two-Stream R-CNN for Action Detection

inferredcites →https://doi.org/10.1007/978-3-319-46723-8_15openalex-snapshot

Learning from Experts: Developing Transferable Deep Features for Patient-Level Lung Cancer Prediction

inferredcites →https://doi.org/10.1007/978-3-319-54526-4_7openalex-snapshot

Classification of Lung Nodule Malignancy Risk on Computed Tomography Images Using Convolutional Neural Network: A Comparison Between 2D and 3D Strategies

inferredcites →https://doi.org/10.1007/978-3-319-59050-9_20openalex-snapshot

Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning

inferredcites →https://doi.org/10.1007/978-3-319-66179-7_64openalex-snapshot

Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.1007/978-3-319-66179-7_69openalex-snapshot

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

inferredcites →https://doi.org/10.1007/978-3-642-22092-0_63openalex-snapshot

3D Shape Analysis for Early Diagnosis of Malignant Lung Nodules

inferredcites →https://doi.org/10.1007/978-3-642-40763-5_31openalex-snapshot

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

inferredcites →https://doi.org/10.1007/s10278-013-9622-7openalex-snapshot

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

inferredcites →https://doi.org/10.1016/j.carj.2014.03.004openalex-snapshot

The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening

inferredcites →https://doi.org/10.1016/j.jksuci.2020.03.013openalex-snapshot

Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning

inferredcites →https://doi.org/10.1016/j.media.2010.02.004openalex-snapshot

A new computationally efficient CAD system for pulmonary nodule detection in CT imagery

inferredcites →https://doi.org/10.1016/j.media.2010.05.005openalex-snapshot

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

inferredcites →https://doi.org/10.1016/j.media.2013.12.001openalex-snapshot

Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

inferredcites →https://doi.org/10.1016/j.media.2017.06.015openalex-snapshot

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

inferredcites →https://doi.org/10.1016/j.media.2017.07.005openalex-snapshot

A survey on deep learning in medical image analysis

inferredcites →https://doi.org/10.1016/j.neunet.2019.03.003openalex-snapshot

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot

Dermatologist-level classification of skin cancer with deep neural networks

inferredcites →https://doi.org/10.1038/ncomms5006openalex-snapshot

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

inferredcites →https://doi.org/10.1056/nejmoa1102873openalex-snapshot

Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

inferredcites →https://doi.org/10.1097/rti.0000000000000097openalex-snapshot

ACR–STR Practice Parameter for the Performance and Reporting of Lung Cancer Screening Thoracic Computed Tomography (CT)

inferredcites →https://doi.org/10.1097/rti.0b013e318256951eopenalex-snapshot

Reader Variability in Identifying Pulmonary Nodules on Chest Radiographs From the National Lung Screening Trial

inferredcites →https://doi.org/10.1109/34.824822openalex-snapshot

Medical image analysis: progress over two decades and the challenges ahead

inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-snapshot

You Only Look Once: Unified, Real-Time Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot

Xception: Deep Learning with Depthwise Separable Convolutions

inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-snapshot

Aggregated Residual Transformations for Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot

YOLO9000: Better, Faster, Stronger

inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-snapshot

Non-local Neural Networks

inferredcites →https://doi.org/10.1109/cvprw.2017.60openalex-snapshot

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot

Fast R-CNN

inferredcites →https://doi.org/10.1109/iccv.2015.170openalex-snapshot

Bilinear CNN Models for Fine-Grained Visual Recognition

inferredcites →https://doi.org/10.1109/iccv.2017.324openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.1109/icmipe.2013.6864494openalex-snapshot

A texture feature analysis for diagnosis of pulmonary nodules using LIDC-IDRI database

inferredcites →https://doi.org/10.1109/isbi45749.2020.9098317openalex-snapshot

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection

inferredcites →https://doi.org/10.1109/tbme.2016.2613502openalex-snapshot

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

inferredcites →https://doi.org/10.1109/tip.2021.3058783openalex-snapshot

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

inferredcites →https://doi.org/10.1109/tmi.2016.2536809openalex-snapshot

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

inferredcites →https://doi.org/10.1109/tmi.2019.2947595openalex-snapshot

A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

inferredcites →https://doi.org/10.1109/tnnls.2019.2892409openalex-snapshot

Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network

inferredcites →https://doi.org/10.1109/tpami.2012.277openalex-snapshot

Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data

inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

inferredcites →https://doi.org/10.1109/tpami.2019.2938758openalex-snapshot

Res2Net: A New Multi-Scale Backbone Architecture

inferredcites →https://doi.org/10.1109/wacv.2018.00079openalex-snapshot

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

inferredcites →https://doi.org/10.1117/12.713754openalex-snapshot

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation

inferredcites →https://doi.org/10.1118/1.2207129openalex-snapshot

Computer‐aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

inferredcites →https://doi.org/10.1118/1.3528204openalex-snapshot

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

inferredcites →https://doi.org/10.1118/1.4907970openalex-snapshot

Large scale validation of the M5L lung CAD on heterogeneous CT datasets

inferredcites →https://doi.org/10.1148/radiol.2017161659openalex-snapshot

Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017

inferredcites →https://doi.org/10.1164/rccm.200901-0076ocopenalex-snapshot

A Randomized Study of Lung Cancer Screening with Spiral Computed Tomography

inferredcites →https://doi.org/10.1378/chest.12-2350openalex-snapshot

Screening for Lung Cancer

inferredcites →https://doi.org/10.3322/caac.21332openalex-snapshot

Cancer statistics, 2016

inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

inferredcites →https://doi.org/10.48550/arxiv.1701.06659openalex-snapshot

DSSD : Deconvolutional Single Shot Detector

inferredcites →https://doi.org/10.5220/0007398700002108openalex-snapshot

A 3D Lung Nodule Candidate Detection by Grouping DCNN 2D Candidates

inferredcites →https://doi.org/10.6004/jnccn.2016.0031openalex-snapshot

NCCN Guidelines Insights: Non–Small Cell Lung Cancer, Version 4.2016

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4756114openalex-extracted

Feature Extraction Methods and Deep Learning Models for Detection of Cancerous Lung Nodules at an Early Stage – Recent Trends and Challenges

inferred← citeshttps://doi.org/10.1002/ima.23209openalex-snapshot

DAG ‐Net: Dual‐Branch Attention‐Guided Network for Multi‐Scale Information Fusion in Lung Nodule Segmentation

inferred← citeshttps://doi.org/10.1002/mp.16183openalex-snapshot

PUNDIT: Pulmonary nodule detection with image category transformation

inferred← citeshttps://doi.org/10.1002/mp.16937openalex-snapshot

Robust explanation supervision for false positive reduction in pulmonary nodule detection

inferred← citeshttps://doi.org/10.1007/978-3-031-16431-6_63openalex-snapshot

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1007/978-3-032-04965-0_58openalex-snapshot

The Missing Piece: A Case for Pre-training in 3D Medical Object Detection

inferred← citeshttps://doi.org/10.1007/978-981-99-1645-0_37openalex-snapshot

ClusterUDA: Latent Space Clustering in Unsupervised Domain Adaption for Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1007/s00259-023-06197-1openalex-snapshot

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in $$[^{18}$$F]FDG PET/CT

inferred← citeshttps://doi.org/10.1007/s00521-026-12020-3openalex-snapshot

TSC-Nodule: a tied non-local and spatial-channel reconstructed faster R-CNN for pulmonary nodule detection

inferred← citeshttps://doi.org/10.1007/s10278-023-00904-yopenalex-snapshot

TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images

inferred← citeshttps://doi.org/10.1007/s11042-022-12908-xopenalex-snapshot

Pulmonary nodules recognition based on parallel cross-convolution

inferred← citeshttps://doi.org/10.1007/s11277-024-11246-5openalex-snapshot

Remote Pulmonary Nodule Detection Based on 6G Network Information System and 3D Visual Transformation

inferred← citeshttps://doi.org/10.1007/s11517-022-02727-5openalex-snapshot

Pulmonary nodule detection based on IR-UNet + +

inferred← citeshttps://doi.org/10.1007/s11517-023-02957-1openalex-snapshot

DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection

inferred← citeshttps://doi.org/10.1007/s12539-026-00873-0openalex-snapshot

A Re-parameterized Network-Based Lightweight Multimodal Fusion Network for Early Lung Cancer Risk Prediction

inferred← citeshttps://doi.org/10.1007/s40747-025-02186-zopenalex-snapshot

MAAN: multi-scale atrous attention network for skin lesion segmentation

inferred← citeshttps://doi.org/10.1016/b978-0-443-33875-5.00004-9openalex-snapshot

Intelligent localization of pelvic lymph nodes

inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112483openalex-snapshot

An intelligent model of pulmonary emphysema detection using adaptive image segmentation and multi-dilated densenet with attention mechanism

inferred← citeshttps://doi.org/10.1016/j.bea.2023.100076openalex-snapshot

Applications of deep learning in disease diagnosis of chest radiographs: A survey on materials and methods

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.103973openalex-snapshot

An improved capuchin search algorithm optimized hybrid CNN-LSTM architecture for malignant lung nodule detection

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.104850openalex-snapshot

Quantum-Involution inspire false positive reduction in pulmonary nodule detection

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106389openalex-snapshot

SF2T:Leveraging Swin Transformer and Two-stream networks for lung nodule detection

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106487openalex-snapshot

DEMF-Net: A dual encoder multi-scale feature fusion network for polyp segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106815openalex-snapshot

Lung-YOLO: Multiscale feature fusion attention and cross-layer aggregation for lung nodule detection

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107358openalex-snapshot

KansNet: Kolmogorov–Arnold Networks and multi slice partition channel priority attention in convolutional neural network for lung nodule detection

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108510openalex-snapshot

Integrated Channel Equally-Divided and Coordinate Attention Feature Pyramid for subtype detection of lung cancer

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108754openalex-snapshot

LNDCNN: A lung nodule detection model based on improved YOLOv7

inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106363openalex-snapshot

Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation

inferred← citeshttps://doi.org/10.1016/j.cmpb.2022.106786openalex-snapshot

One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image

inferred← citeshttps://doi.org/10.1016/j.cmpb.2023.107748openalex-snapshot

MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106059openalex-snapshot

Two-stage lung nodule detection framework using enhanced UNet and convolutional LSTM networks in CT images

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.106587openalex-snapshot

WS-LungNet: A two-stage weakly-supervised lung cancer detection and diagnosis network

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107878openalex-snapshot

A novel open-source CADs platform for 3D CT pulmonary analysis

inferred← citeshttps://doi.org/10.1016/j.compmedimag.2024.102438openalex-snapshot

An Anthropomorphic Diagnosis System of Pulmonary Nodules using Weak Annotation-Based Deep Learning

inferred← citeshttps://doi.org/10.1016/j.dsp.2025.105879openalex-snapshot

APU-Net: A U-Net enhanced network with dynamic feature fusion and pyramid cross-attention mechanism for polyp segmentation

inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107597openalex-snapshot

MEDS-Net: Multi-encoder based self-distilled network with bidirectional maximum intensity projections fusion for lung nodule detection

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111577openalex-snapshot

Medical artificial intelligence for early detection of lung cancer: A survey

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112885openalex-snapshot

Tissue-contrastive semi-masked autoencoders for segmentation pretraining on chest computed tomography

inferred← citeshttps://doi.org/10.1016/j.eswa.2022.118672openalex-snapshot

An attentive and adaptive 3D CNN for automatic pulmonary nodule detection in CT image

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121890openalex-snapshot

Pulmonary nodule detection based on model fusion and adaptive false positive reduction

inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127341openalex-snapshot

False positive reduction for lung nodule detection using 3D Channel-Spatial attention model with multi-descriptor-based refinement

inferred← citeshttps://doi.org/10.1016/j.inffus.2023.02.012openalex-snapshot

CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery

inferred← citeshttps://doi.org/10.1016/j.jare.2024.11.033openalex-snapshot

Full dimensional dynamic 3D convolution and point cloud in pulmonary nodule detection

inferred← citeshttps://doi.org/10.1016/j.jbi.2025.104931openalex-snapshot

From image to report: automating lung cancer screening interpretation and reporting with vision-language models

inferred← citeshttps://doi.org/10.1016/j.jvcir.2021.103356openalex-snapshot

Fixation prediction for advertising images: Dataset and benchmark

inferred← citeshttps://doi.org/10.1016/j.media.2021.102287openalex-snapshot

SCPM-Net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching

inferred← citeshttps://doi.org/10.1016/j.media.2022.102444openalex-snapshot

Recent advances and clinical applications of deep learning in medical image analysis

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128827openalex-snapshot

CSSANet: A channel shuffle slice-aware network for pulmonary nodule detection

inferred← citeshttps://doi.org/10.1016/j.patcog.2022.109261openalex-snapshot

A pyramid input augmented multi-scale CNN for GGO detection in 3D lung CT images

inferred← citeshttps://doi.org/10.1016/j.patcog.2024.110553openalex-snapshot

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

inferred← citeshttps://doi.org/10.1016/j.ress.2022.108972openalex-snapshot

Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions

inferred← citeshttps://doi.org/10.1038/s41467-025-59200-8openalex-snapshot

Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning

inferred← citeshttps://doi.org/10.1038/s41597-024-03658-6openalex-snapshot

A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation

inferred← citeshttps://doi.org/10.1038/s41597-024-03851-7openalex-snapshot

A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset

inferred← citeshttps://doi.org/10.1038/s41598-025-97187-wopenalex-snapshot

Lung nodule detection using a multi-scale convolutional neural network and global channel spatial attention mechanisms

inferred← citeshttps://doi.org/10.1038/s43856-023-00388-5openalex-snapshot

Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans

inferred← citeshttps://doi.org/10.1063/5.0322683openalex-snapshot

A survey on computer-aided diagnosis system for lung cancer

inferred← citeshttps://doi.org/10.1088/1361-6560/ac568eopenalex-snapshot

Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

inferred← citeshttps://doi.org/10.1088/1361-6560/ad2b96openalex-snapshot

S-Net: an S-shaped network for nodule detection in 3D CT images

inferred← citeshttps://doi.org/10.1088/2057-1976/ad9154openalex-snapshot

A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage –the recent trends and challenges

inferred← citeshttps://doi.org/10.1109/access.2021.3109860openalex-snapshot

Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System

inferred← citeshttps://doi.org/10.1109/access.2023.3321966openalex-snapshot

An Anchor-Free Lightweight Object Detection Network

inferred← citeshttps://doi.org/10.1109/access.2025.3564633openalex-snapshot

A Review on Lung Cancer Classification Using Deep Learning Techniques

inferred← citeshttps://doi.org/10.1109/bibm55620.2022.9995100openalex-snapshot

MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Lung Nodule Detection

inferred← citeshttps://doi.org/10.1109/bibm58861.2023.10385637openalex-snapshot

DFNodule: a Novel Deformable Faster R-CNN for Lung Nodule Detection

inferred← citeshttps://doi.org/10.1109/bibm62325.2024.10822571openalex-snapshot

PN-Quant: An Automated Pulmonary Nodule Quantification Method for Full-Size CT Scans

inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356378openalex-snapshot

DTFNet: A Dual-Path Transformer-CNN Network for Robust Colorectal Polyp Segmentation

inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356486openalex-snapshot

Attention-Guided Dual-Path Network for Pulmonary Nodule Detection in CT Scans

inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356692openalex-snapshot

Recurrent Visual Feature Extraction and Stereo Attentions for CT Report Generation

inferred← citeshttps://doi.org/10.1109/cscwd68734.2026.11581455openalex-snapshot

FAM-Net: A Frequency-Aware Multi-Scale Attention Network for Polyp Segmentation

inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.00311openalex-snapshot

Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline

inferred← citeshttps://doi.org/10.1109/cvpr52734.2025.01459openalex-snapshot

Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images

inferred← citeshttps://doi.org/10.1109/euvip58404.2023.10323041openalex-snapshot

Automatic lung nodule classification in CT images using Two-stage CNNs and Soft-voting of Multi-scale Classifiers

inferred← citeshttps://doi.org/10.1109/i-smac52330.2021.9641051openalex-snapshot

A Review on Identification and Classification of Lung Nodules

inferred← citeshttps://doi.org/10.1109/icacte62428.2024.10871839openalex-snapshot

MLS-YOLOv8m: A Multi-Scale Feature Enhancement Method for Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11464535openalex-snapshot

MQ-UNet: Boundary-Aware Adapter Tuning of SAM2 for Colorectal Polyp Segmentation

inferred← citeshttps://doi.org/10.1109/iccsi62669.2024.10799277openalex-snapshot

A Channel Fusion Convolutional Neural Network for Lung Nodule Detection

inferred← citeshttps://doi.org/10.1109/icesc60852.2024.10689724openalex-snapshot

Diagnosis of Lung Cancer Detection Using Various Deep Learning Models: A Survey

inferred← citeshttps://doi.org/10.1109/icftic59930.2023.10456357openalex-snapshot

CM-Net: Pulmonary Nodule Detection Network with Convolution and MLP

inferred← citeshttps://doi.org/10.1109/icis64839.2024.10887472openalex-snapshot

An Efficient Approach to Detecting Lung Nodules Using Swin Transformer

inferred← citeshttps://doi.org/10.1109/icmi65310.2025.11141220openalex-snapshot

A Multiscale Anchor-Free 3D CNN Integrated with Denoising for Early Lung Nodule Detection in Low-Dose CT Images

inferred← citeshttps://doi.org/10.1109/icrcv59470.2023.10329010openalex-snapshot

Primary brain tumors Image segmentation based on 3D-UNET with deep supervision and 3D brain modeling

inferred← citeshttps://doi.org/10.1109/icvrv67992.2025.00011openalex-snapshot

Skin Lesion Segmentation Model with Large Receptive Fields and Multi-Scale Features

inferred← citeshttps://doi.org/10.1109/ijcnn54540.2023.10191294openalex-snapshot

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

inferred← citeshttps://doi.org/10.1109/ijcnn55064.2022.9892716openalex-snapshot

Pulmonary Nodule Classification with Multi-View Convolutional Vision Transformer

inferred← citeshttps://doi.org/10.1109/ijcnn60899.2024.10650985openalex-snapshot

CLCT-GAN: Strong-Weak Contrastive Learning for Reconstructing CT Images from Radiographs

inferred← citeshttps://doi.org/10.1109/isbi53787.2023.10230631openalex-snapshot

FFNET: An End-To-End Framework Based on Feature Pyramid Network and Filter Network for Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1109/ised67359.2025.11405228openalex-snapshot

Evaluation of YOLOv11 and CNN Approaches for Lung Cancer Detection in CT Scan Data

inferred← citeshttps://doi.org/10.1109/itme60234.2023.00152openalex-snapshot

Spherical Detection Frame and Attention-Based Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1109/jbhi.2021.3131671openalex-snapshot

MSANet: Multiscale Aggregation Network Integrating Spatial and Channel Information for Lung Nodule Detection

inferred← citeshttps://doi.org/10.1109/jbhi.2022.3198509openalex-snapshot

A Cascaded Multi-Stage Framework for Automatic Detection and Segmentation of Pulmonary Nodules in Developing Countries

inferred← citeshttps://doi.org/10.1109/jbhi.2024.3402186openalex-snapshot

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging

inferred← citeshttps://doi.org/10.1109/jbhi.2024.3507360openalex-snapshot

OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN

inferred← citeshttps://doi.org/10.1109/nmitcon58196.2023.10276037openalex-snapshot

Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep Learning

inferred← citeshttps://doi.org/10.1109/tbme.2025.3619056openalex-snapshot

An Electromagnetic Navigation Surgical Robotic System (ENSRS) for Transthoracic Puncture of Small Pulmonary Nodules

inferred← citeshttps://doi.org/10.1109/tcbb.2023.3253713openalex-snapshot

SGDA: Towards 3-D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention

inferred← citeshttps://doi.org/10.1109/tcsvt.2024.3427645openalex-snapshot

Interpretable Heterogeneous Teacher-Student Learning Framework for Hybrid-Supervised Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1109/tetci.2024.3367821openalex-snapshot

YOLO-SM: A Lightweight Single-Class Multi-Deformation Object Detection Network

inferred← citeshttps://doi.org/10.1109/tmi.2023.3329464openalex-snapshot

FAOT-Net: A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning

inferred← citeshttps://doi.org/10.1109/tnb.2023.3278706openalex-snapshot

3D ARCNN: An Asymmetric Residual CNN for False Positive Reduction in Pulmonary Nodule

inferred← citeshttps://doi.org/10.1109/tnnls.2022.3230821openalex-snapshot

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation

inferred← citeshttps://doi.org/10.1109/tnnls.2023.3303395openalex-snapshot

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck

inferred← citeshttps://doi.org/10.1109/tpami.2024.3449994openalex-snapshot

Gaseous Object Detection

inferred← citeshttps://doi.org/10.1117/12.3008472openalex-snapshot

Explainable AI for lung nodule detection and classification in CT images

inferred← citeshttps://doi.org/10.1117/12.3031394openalex-snapshot

HARANet: a skin lesion image segmentation network based on hybrid reverse attention

inferred← citeshttps://doi.org/10.1117/12.3033704openalex-snapshot

Space to depth convolution based discriminative pulmonary nodules classification

inferred← citeshttps://doi.org/10.1145/3613330.3613337openalex-snapshot

Tiny Object Detector for Pulmonary Nodules based on YOLO

inferred← citeshttps://doi.org/10.1145/3616901.3616996openalex-snapshot

OSANet:A One-Shot Aggregation Network for Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.1155/2022/8903037openalex-snapshot

Pulmonary Nodule Detection Based on Multiscale Feature Fusion

inferred← citeshttps://doi.org/10.1186/s12880-025-01761-7openalex-snapshot

Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning

inferred← citeshttps://doi.org/10.1186/s40510-025-00598-8openalex-snapshot

Accuracy comparative study of automatic landmarking and diagnostic models on lateral cephalograms

inferred← citeshttps://doi.org/10.1360/ssi-2021-0384openalex-snapshot

Damage assessment with global differences and local attention

inferred← citeshttps://doi.org/10.1364/boe.504875openalex-snapshot

Multi-kernel driven 3D convolutional neural network for automated detection of lung nodules in chest CT scans

inferred← citeshttps://doi.org/10.20517/evcna.2024.51openalex-snapshot

The times they are AI-changing: AI-powered advances in the application of extracellular vesicles to liquid biopsy in breast cancer

inferred← citeshttps://doi.org/10.3233/web-230008aopenalex-snapshot

An effective accurate lung nodule detection of CT images using extensible hunt optimization based deep CNN

inferred← citeshttps://doi.org/10.3389/fnut.2025.1752250openalex-snapshot

Tongue image analysis for accurate prediction of nutritional risk screening in cancer patients during radiochemotherapy: a feature selection network and aliasing attention mechanism approach

inferred← citeshttps://doi.org/10.3390/app13106235openalex-snapshot

An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images

inferred← citeshttps://doi.org/10.3390/cancers17040621openalex-snapshot

Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies

inferred← citeshttps://doi.org/10.3390/diagnostics12112660openalex-snapshot

A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening

inferred← citeshttps://doi.org/10.3390/electronics12102333openalex-snapshot

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

inferred← citeshttps://doi.org/10.3390/informatics12030065openalex-snapshot

DA OMS-CNN: Dual-Attention OMS-CNN with 3D Swin Transformer for Early-Stage Lung Cancer Detection

inferred← citeshttps://doi.org/10.3390/math11102344openalex-snapshot

ELCT-YOLO: An Efficient One-Stage Model for Automatic Lung Tumor Detection Based on CT Images

inferred← citeshttps://doi.org/10.4108/eetel.4775openalex-snapshot

Deep learning-based lung nodule detection: a review

inferred← citeshttps://doi.org/10.4108/eetpht.10.5265openalex-snapshot

Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.