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Found 8 papers, 6 papers with code
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
Our tool contributes to the wider adoption of AI-based Gleason grading within the research community and paves the way for broader clinical application of deep learning models in digital pathology.
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data
Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms.
Towards a Guideline for Evaluation Metrics in Medical Image Segmentation
In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation.
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks
However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines.
MISeval: a Metric Library for Medical Image Segmentation Evaluation
Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation.
Assessing the Role of Random Forests in Medical Image Segmentation
Our results indicate that random forest approaches are a good alternative to deep convolutional neural networks and, thus, allow the usage of medical image segmentation without a GPU.
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide.
