All Stories

  1. Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
  2. FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
  3. A multi-centre polyp detection and segmentation dataset for generalisability assessment
  4. Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation
  5. MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
  6. Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
  7. TGANet: Text-Guided Attention for Improved Polyp Segmentation
  8. A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation
  9. NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
  10. Kvasir-Capsule, a video capsule endoscopy dataset
  11. A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
  12. Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge
  13. LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
  14. Progressively Normalized Self-Attention Network for Video Polyp Segmentation
  15. Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
  16. The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
  17. HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy
  18. An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning Applied to Gastrointestinal Tract Abnormality Classification
  19. DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
  20. 3D CNN Design for the Classification of Alzheimer’s Disease Using Brain MRI and PET
  21. Kvasir-SEG: A Segmented Polyp Dataset
  22. Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases
  23. Development of an Efficient Cascade Pathological-Brain Detection System using a Median Filter and Quadratic Discriminant Analysis
  24. Efficient Cascade Model for Pathological Brain Image Detection by Magnetic Resonance Imaging
  25. Alzheimer's Disease Detection Using Sparse Autoencoder, Scale Conjugate Gradient and Softmax Output Layer with Fine Tuning
  26. Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier
  27. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network