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Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation
20/20 checkable references clean · checked 2026-08-06
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
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The 20 checked references that resolve
resolves10.1007/s10434-000-0705-6
Gastrointestinal Stromal Tumors: Current Diagnosis, Biologic Behavior, and Management
resolves10.1007/s10120-019-00928-w
Digital image analysis-based scoring system for endoscopic ultrasonography is useful in predicting gastrointestinal stromal tumors
resolves10.1111/jgh.15653
Convolutional neural network‐based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images
resolves10.3390/s20082169
Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid
resolves10.1109/ICASSP.2019.8682773
Implicit Fusion by Joint Audiovisual Training for Emotion Recognition in Mono Modality
resolves10.1109/JBHI.2021.3097721
Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer's Disease
resolves10.1002/mp.12134
Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images
resolves10.1109/TMI.2021.3139023
Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks
resolves10.1109/TUFFC.2022.3190012
Multi-Attribute Attention Network for Interpretable Diagnosis of Thyroid Nodules in Ultrasound Images
resolves10.1016/j.bspc.2022.104536
Feature generation and multi-sequence fusion based deep convolutional network for breast tumor diagnosis with missing MR sequences
resolves10.1609/aaai.v34i07.6619
Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis
resolves10.1109/ACCESS.2020.2996407
Adversarial Attentive Multi-Modal Embedding Learning for Image-Text Matching
resolves10.1159/000520032
Differentiating Gastrointestinal Stromal Tumors from Leiomyomas Using a Neural Network Trained on Endoscopic Ultrasonography Images
The 21 references without a DOI — listed, not checked
no DOI — not checkedCd117: a sensitive marker for gastrointestinal stromal tumors that is more specific than cd34
no DOI — not checkedref3
no DOI — not checkedApplication of a convolutional neural network in the diagnosis of gastric mesenchymal tumors on endoscopic ultrasonography images
no DOI — not checkedAn artificial intelligence system for distinguishing between gastrointestinal stromal tumors and leiomyomas using endoscopic ultrasonography
no DOI — not checkedA review: Deep learning for medical image segmentation using multi-modality fusion
no DOI — not checkedSurvival prediction of brain cancer with incomplete radiology, pathology, genomic, and demographic data
no DOI — not checkedAcn: adversarial co-training network for brain tumor segmentation with missing modalities
no DOI — not checkedHeteromodal variational encoder-decoder for joint modality completion and segmentation
no DOI — not checkedMultimodal image-text matching improves retrieval-based chest x-ray report generation
no DOI — not checkedMulti-modal sequence learning for alzheimer's disease progression prediction with incomplete variable-length longitudinal data
no DOI — not checkedMissing modality imagination network for emotion recognition with uncertain missing modalities
no DOI — not checkedMulti-modal learning with missing modality via shared-specific feature modelling
no DOI — not checkedAssessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages
no DOI — not checkedref30
no DOI — not checkedref33
no DOI — not checkedref34
no DOI — not checkedref35
no DOI — not checkedref36
no DOI — not checkedVisualizing data using t-sne
no DOI — not checkedAutoencoders, unsupervised learning, and deep architectures
no DOI — not checkedMultimodal generative models for scalable weaklysupervised learning
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