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Found 13 papers, 4 papers with code
Adversarially Robust Feature Learning for Breast Cancer Diagnosis
It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data.
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments.
Human not in the loop: objective sample difficulty measures for Curriculum Learning
Curriculum learning is a learning method that trains models in a meaningful order from easier to harder samples.
PGFed: Personalize Each Client's Global Objective for Federated Learning
Based on our observation, in this work, we propose Personalized Global Federated Learning (PGFed), a novel personalized FL framework that enables each client to personalize its own global objective by explicitly and adaptively aggregating the empirical risks of itself and other clients.
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning
We also show that pretraining on extended (but not labeled) brain MRI data outperforms pretraining on natural images.
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification
To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance.
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images
These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized.
Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis
In this work, we propose an end-to-end Curriculum Learning (CL) strategy in task space for classifying the three categories of Full-Field Digital Mammography (FFDM), namely Malignant, Negative, and False recall.
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture Classification
We evaluate our method through extensive experiments on a classification task of elbow fracture with a dataset of 1, 964 images.
Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images
We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics.
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning
We also introduce a method to flexibly control the focus of training APPLE between global and local objectives.
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification
Machine learning in medical research, by nature, needs careful attention on obeying the regulations of data privacy, making it difficult to train a machine learning model over gathered data from different medical centers.
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning
We also introduce a method to flexibly control the focus of training APPLE between global and local objectives.
