RFIA-Net: Rich CNN-transformer network based on asymmetric fusion feature aggregation to classify stage I multimodality oesophageal cancer images

[1]  Yunchao Tang,et al.  Fruit detection and positioning technology for a Camellia oleifera C. Abel orchard based on improved YOLOv4-tiny model and binocular stereo vision , 2022, Expert Syst. Appl..

[2]  Asif Ekbal,et al.  Multi-modality helps in crisis management: An attention-based deep learning approach of leveraging text for image classification , 2022, Expert Syst. Appl..

[3]  Mantripragada Yaswanth Bhanu Murthy,et al.  Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis , 2021, Biomedical Engineering Letters.

[4]  Mingyou Chen,et al.  3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM , 2021, Comput. Electron. Agric..

[5]  Hadil Abu Khalifeh,et al.  A novel computer-aided diagnostic system for accurate detection and grading of liver tumors , 2021, Scientific Reports.

[6]  R. Maskeliūnas,et al.  Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network , 2021, Diagnostics.

[7]  G. Valdes,et al.  Artificial intelligence and machine learning for medical imaging: A technology review. , 2021, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[8]  A. Darzi,et al.  Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis , 2021, npj Digital Medicine.

[9]  Quoc V. Le,et al.  EfficientNetV2: Smaller Models and Faster Training , 2021, ICML.

[10]  Shengchang Ji,et al.  EWNet: An early warning classification framework for smart grid based on local-to-global perception , 2021, Neurocomputing.

[11]  Samuel G. Finlayson,et al.  Code-free deep learning for multi-modality medical image classification , 2021, Nature Machine Intelligence.

[12]  May D. Wang,et al.  Multimodal deep learning models for early detection of Alzheimer’s disease stage , 2021, Scientific Reports.

[13]  Muhammet Fatih Aslan,et al.  CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection , 2020, Applied Soft Computing.

[14]  Guoxin Zhang,et al.  Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images. , 2020, Gastrointestinal endoscopy.

[15]  Limin Luo,et al.  ELNet: Automatic classification and segmentation for esophageal lesions using convolutional neural network , 2020, Medical Image Anal..

[16]  An Hu,et al.  Brain tumor diagnosis based on metaheuristics and deep learning , 2020, Int. J. Imaging Syst. Technol..

[17]  Hyunjin Park,et al.  Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[18]  Junzhou Huang,et al.  Deep convolutional neural network for survival analysis with pathological images , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[19]  John A. Evans,et al.  The role of endoscopy in the assessment and treatment of esophageal cancer. , 2013, Gastrointestinal endoscopy.

[20]  Wen-Yen Huang,et al.  Multimodality and nanoparticles in medical imaging. , 2011, Dalton transactions.

[21]  A. Cowen,et al.  Advances in computed radiography systems and their physical imaging characteristics. , 2007, Clinical radiology.

[22]  Sridha Sridharan,et al.  Multi-modal semantic image segmentation , 2021, Comput. Vis. Image Underst..

[23]  M. Fujishiro,et al.  Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. , 2019, Gastrointestinal endoscopy.

[24]  I. I. Rushakov,et al.  Computed Tomography , 2019, Compendium of Biomedical Instrumentation.