MIA-COVID19D21
* *AI-Enabled Medical Image Analysis and COVID-19 Diagnosis
* 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images, A
* Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification
* Advanced 3D Deep Non-Local Embedded System for Self-Augmented X-Ray-based COVID-19 Assessment
* Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans
* CMC-COV19D: Contrastive Mixup Classification for COVID-19 Diagnosis
* COVID19 Diagnosis using AutoML from 3D CT scans
* Evaluating volumetric and slice-based approaches for COVID-19 detection in chest CTs
* Hierarchical Classification System for the Detection of Covid-19 from Chest X-Ray Images, A
* hybrid and fast deep learning framework for Covid-19 detection via 3D Chest CT Images, A
* Intelligent Radiomic Analysis of Q-SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients
* MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
* Residual Dilated U-net For The Segmentation Of COVID-19 Infection From CT Images
* TeliNet: Classifying CT scan images for COVID-19 diagnosis
* transformer-based framework for automatic COVID19 diagnosis in chest CTs, A
* Value of Visual Attention for COVID-19 Classification in CT Scans, The
* Visual interpretability analysis of Deep CNNs using an Adaptive Threshold method on Diabetic Retinopathy images
17 for MIA-COVID19D21
MIA-COVID19D22
* *AI-Enabled Medical Image Analysis and COVID-19 Diagnosis
* Ai-mia: Covid-19 Detection and Severity Analysis Through Medical Imaging
* Automatic Grading of Cervical Biopsies by Combining Full and Self-supervision
* Boosting Covid-19 Severity Detection with Infection-aware Contrastive Mixup Classification
* CCRL: Contrastive Cell Representation Learning
* CMC_V2: Towards More Accurate Covid-19 Detection with Discriminative Video Priors
* CNR-IEMN-CD and CNR-IEMN-CSD Approaches for Covid-19 Detection and Covid-19 Severity Detection from 3d Ct-scans
* Covid Detection and Severity Prediction with 3d-convnext and Custom Pretrainings
* Deep Wavelet Network for High-resolution Microscopy Hyperspectral Image Reconstruction, A
* Explainable Model for Localization of Spiculation in Lung Nodules
* Fusion: Fully Unsupervised Test-time Stain Adaptation via Fused Normalization Statistics
* Harmonization of Diffusion MRI Data Obtained with Multiple Head Coils Using Hybrid Cnns
* Medical Image Segmentation: A Review of Modern Architectures
* Medical Image Super Resolution by Preserving Interpretable and Disentangled Features
* Multi-label Attention Map Assisted Deep Feature Learning for Medical Image Classification
* Multi-scale Attention-based Multiple Instance Learning for Classification of Multi-gigapixel Histology Images
* PVT-COV19D: Covid-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer
* Relieving Pixel-wise Labeling Effort for Pathology Image Segmentation with Self-training
* Representation Learning with Information Theory to Detect Covid-19 and Its Severity
* Self-supervised Pretraining for 2d Medical Image Segmentation
* Spatial-slice Feature Learning Using Visual Transformer and Essential Slices Selection Module for Covid-19 Detection of Ct Scans in the Wild
* Two-stage Covid19 Classification Using Bert Features
* Unsupervised Domain Adaptation Using Feature Disentanglement and Gcns for Medical Image Classification
* Using a 3d Resnet for Detecting the Presence and Severity of Covid-19 from CT Scans
* Using Whole Slide Image Representations from Self-supervised Contrastive Learning for Melanoma Concordance Regression
* Variability Matters: Evaluating Inter-rater Variability in Histopathology for Robust Cell Detection
* When CNN Meet with VIT: Towards Semi-supervised Learning for Multi-class Medical Image Semantic Segmentation
27 for MIA-COVID19D22