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Found 10 papers, 6 papers with code
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning
We propose a regression-based deep learning approach for the endoscopic evaluation of UC according to the Mayo endoscopic score (MES).
Ranked #2 on
Image Classification
on LIMUC
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation
Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain.
Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation
Experimental evaluations show that models trained with CDW-CE outperform the models trained with conventional categorical cross-entropy and other commonly used loss functions which are designed for the ordinal regression problems.
Ranked #1 on
Image Classification
on LIMUC
BARFED: Byzantine Attack-Resistant Federated Averaging Based on Outlier Elimination
Although many defense algorithms have recently been proposed to address these attacks, they often make strong assumptions that do not agree with the nature of federated learning, such as Non-IID datasets.
Effect of Input Size on the Classification of Lung Nodules Using Convolutional Neural Networks
In this study, we proposed a framework that analyzes CT lung screenings using convolutional neural networks (CNNs) to reduce false positives.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks
3D CNNs are preferred over 2D CNNs because data are in 3D, and 2D convolutional operations may result in information loss.
