Controlling false positives in multiple instance learning: The “c-rule” approach

This publication has 15 references indexed in Scilit:

• Cumulative Probability of False-Positive Results After 10 Years of Screening With Digital Breast Tomosynthesis vs Digital Mammography
  JAMA Network Open, 2022
• Reducing False-Positive Screening MRI Rates in Women with Extremely Dense Breasts
  Radiology, 2021
• Multiple instance classification: Bag noise filtering for negative instance noise cleaning
  Information Sciences, 2021
• Learning From Large-Scale Noisy Web Data With Ubiquitous Reweighting for Image Classification
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
• Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD
  Journal of Digital Imaging, 2019
• Multiple instance learning: A survey of problem characteristics and applications
  Pattern Recognition, 2018
• Single- vs. multiple-instance classification
  Pattern Recognition, 2015
• Multiple instance classification: Review, taxonomy and comparative study
  Artificial Intelligence, 2013
• A review of multi-instance learning assumptions
  The Knowledge Engineering Review, 2010
• Solving the multiple instance problem with axis-parallel rectangles
  Artificial Intelligence, 1997