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Found 6 papers, 1 papers with code
Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model
We can conclude that attribute scores and visual prototypes enhance confidence in the model.
Pre-examinations Improve Automated Metastases Detection on Cranial MRI
Materials and methods: First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion of also the native T1-weighted images, T2-weighted images, and FLAIR sequences of both time points (CNNdual_all). Second, results were compared with the corresponding single time approaches, in which the CNN was provided exclusively the respective sequences of the diagnosis MRI. Casewise diagnostic performance parameters were calculated from 5-fold cross-validation.
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation.
Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation
In machine learning larger databases are usually associated with higher classification accuracy due to better generalization.
On Leakage in Machine Learning Pipelines
Machine learning (ML) provides powerful tools for predictive modeling.
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets.
