Non-uniform Sampling-Based Breast Cancer Classification

Santiago Posso Murillo, Oscar Skean, Luis G. Sanchez Giraldo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The emergence of deep learning models and their remarkable success in visual object recognition and detection have fueled the medical imaging community’s interest in integrating these algorithms to improve medical screening and diagnosis. However, natural images, which have been the main focus of deep learning models, and medical images, such as mammograms, have fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions. To fit these images to deep learning approaches, they must be heavily downsampled. Otherwise, models that address high-resolution mammograms require many exams and complex architectures. Spatially resizing mammograms leads to losing discriminative details that are essential for accurate diagnosis. To address this limitation, we develop an approach to exploit the relative importance of pixels in mammograms by conducting non-uniform sampling based on task-salient regions generated by a convolutional network. Classification results demonstrate that non-uniformly sampled images preserve discriminant features requiring lower resolutions to outperform their uniformly sampled counterparts.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsXiaohuan Cao, Xi Ouyang, Xuanang Xu, Islem Rekik, Zhiming Cui
Number of pages11
StatePublished - 2024
Event14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023 - Vancouver, Canada
Duration: Oct 8 2023Oct 8 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14349 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Mammogram Classification
  • Non-Uniform Sampling
  • Saliency

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Non-uniform Sampling-Based Breast Cancer Classification'. Together they form a unique fingerprint.

Cite this