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Found 10 papers, 5 papers with code
Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images.
NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images
In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg.
A global analysis of metrics used for measuring performance in natural language processing
Our results suggest that the large majority of natural language processing metrics currently used have properties that may result in an inadequate reflection of a models' performance.
Improving Mask R-CNN for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images
Recently, instance-aware segmentation methods such as Mask R-CNN have been proposed to enable unified instance detection and segmentation, even in overlapping cases.
CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images
Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality.
A critical analysis of metrics used for measuring progress in artificial intelligence
Our results suggest that the large majority of metrics currently used have properties that may result in an inadequate reflection of a models' performance.
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification
Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification.
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity
As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing.
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations.
