| dc.description.abstract | Whole Slide Image (WSI) classification involves analyzing large digital images of tissue
samples or other biological specimens captured by scanning the entire slide at ultra resolution.
The objective of WSI classification is to accurately identify and classify different regions of
interest within the image, such as cancerous or normal tissue, as well as specific cell types and
structures.
WSI classification has numerous applications in the field of digital pathology and
medical research, including aiding in the diagnosis and treatment of cancer, identifying potential
drug targets, and enabling personalized medicine. Recent advances in deep learning techniques
have led to significant improvements in WSI classification performance, with state-of-the-art
models achieving high levels of accuracy and robustness.
However, current WSI classification pipelines typically require a large amount of
labeled data for training, which can be both costly and time-consuming to obtain. Moreover,
simply training a model on a large dataset does not guarantee good generalization performance.
In fact, the generalization ability of WSI models is often limited when trained on large datasets,
due to issues such as overfitting to the training data and difficulty in learning robust features.
In this work, we propose a framework to deal with the aforementioned issues. We incorporate
ensemble classifier approach integrated with meta-learning, which enables the model to learn
from a limited number of labeled samples while still achieving remarkable performance.
Furthermore, we also propose a simple cluster-based noise injection to force the model to learn
more robust and diverse features. Through comprehensive experiments, we demonstrate the
effectiveness of our approach with only a small number of samples on two publicly available
datasets for WSI classification tasks. | en_US |