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Found 7 papers, 1 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.
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.
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.
