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Found 14 papers, 1 papers with code
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction
In this study we aim to improve automated segmentation in CBCTs through multi-task learning.
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.
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.
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
Thyroid cancer is currently the fifth most common malignancy diagnosed in women.
Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure
For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack.
Anomaly Detection in Medical Imaging -- A Mini Review
The increasing digitization of medical imaging enables machine learning based improvements in detecting, visualizing and segmenting lesions, easing the workload for medical experts.
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.
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
In addition, we identify currently unavailable methods with potential for future applications.
Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
We investigate if models can be trained on virtual (or a mixture of virtual and real) samples to improve overall accuracy in a setting with limited labeled training data.
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.
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios.
CNN Cascades for Segmenting Whole Slide Images of the Kidney
Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems.
