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Found 7 papers, 1 papers with code
MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification
Here we conduct a large study incorporating 10 different data set configurations, two different feature extraction approaches (supervised and self-supervised), stain normalization and two multiple instance learning architectures.
A Commons-Compatible Implementation of the Sharing Economy: Blockchain-Based Open Source Mediation
To detect the most commons-compatible implementation, the different implementation options through conventional platform intermediators, an open source blockchain with PoW as well as Interlaces' permissioned blockchain approach, are compared.
Inflation forecasting with attention based transformer neural networks
Our results demonstrate that a transformer based neural network can outperform classical regression and machine learning models in certain inflation rates and forecasting horizons.
MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis
Multiple instance learning exhibits a powerful approach for whole slide image-based diagnosis in the absence of pixel- or patch-level annotations.
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential
Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data.
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions.
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.
