Search Results for author:
Found 5 papers, 2 papers with code
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears.
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification
Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training.
Topologically Regularized Multiple Instance Learning to Harness Data Scarcity
In biomedical data analysis, Multiple Instance Learning (MIL) models have emerged as a powerful tool to classify patients' microscopy samples.
Anomaly-aware multiple instance learning for rare anemia disorder classification
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations.
GANs for Medical Image Analysis
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification.
