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Found 3 papers, 1 papers with code
BRAIxDet: Learning to Detect Malignant Breast Lesion with Incomplete Annotations
Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it.
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models
On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity.
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views.
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