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11/10/2022
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 ...
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06/09/2022
Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations Future Potential
Digital whole slides images contain an enormous amount of information pr...
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04/22/2022
Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
Thyroid cancer is currently the fifth most common malignancy diagnosed i...
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10/11/2021
Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure
Enterprises and labs performing computationally expensive data science a...
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08/25/2021
Anomaly Detection in Medical Imaging – A Mini Review
The increasing digitization of medical imaging enables machine learning ...
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12/15/2020
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 generat...
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04/30/2020
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
Image analysis in the field of digital pathology has recently gained inc...
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04/27/2020
Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities
Novel imaging technologies raise many questions concerning the adaptatio...
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04/23/2020
An Asymetric 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 facilitat...
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05/25/2018
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
Recently, generative adversarial networks exhibited excellent performanc...
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08/01/2017