Search Results for author:
Found 10 papers, 6 papers with code
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels.
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering.
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images
Finally, the central server aggregates the results, retrieving the average of models' weights and updating the model without having access to individual hospitals' weights.
Hospital-Agnostic Image Representation Learning in Digital Pathology
These variabilities are assumed to cause a domain shift in the images of different hospitals.
Magnification Generalization for Histopathology Image Embedding
However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.
Breast Cancer Histology Image Classification
Classification Of Breast Cancer Histology Images
+3
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
However, sampling from stochastic distributions of data rather than sampling merely from the existing embedding instances can provide more discriminative information.
Dimensionality Reduction
Histopathological Image Classification
+1
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100, 000 patches.
Dimensionality Reduction
Histopathological Image Classification
+1
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method.
Classification Of Breast Cancer Histology Images
Dimensionality Reduction
+3
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.
