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Found 6 papers, 1 papers with code
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e. g., bulk RNA-seq) for quantifying gene expressions.
PHG-Net: Persistent Homology Guided Medical Image Classification
The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion.
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
Therefore, it is still hard to perform common machine learning such as logistic regression and neural networks in high performance.
Privacy-Preserving Distributed Machine Learning Made Faster
So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one.
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation
Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.
