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Found 8 papers, 0 papers with code
Uncertainty Quantified Deep Learning for Predicting Dice Coefficient of Digital Histopathology Image Segmentation
Results from this study suggest that linear models can learn coefficients of uncertainty quantified deep learning and correlations ((Spearman's correlation (p<0. 05)) to predict Dice scores of specific regions of medical images.
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
The objective of this study is to develop a machine learning (ML) approach adept at soybean [\textit{Glycine max} L.
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images
Deep learning (DL) models for disease classification or segmentation from medical images are increasingly trained using transfer learning (TL) from unrelated natural world images.
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data
Finally, a look to the previous generation of generative models in the form of probabilistic principal component analysis is inspired, and revisited for the same data-sets and shown to work really well for discriminating anomalies based on likelihood in a fully unsupervised fashion compared with pixelCNN++, GLOW, and real NVP with less complexity and faster training.
Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions.
Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to its active layer morphology.
Interpretable Deep Learning applied to Plant Stress Phenotyping
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce.
An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPS
Modern distributed cyber-physical systems (CPSs) encounter a large variety of physical faults and cyber anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems.
