• Corpus ID: 239050132

Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis

@article{Luo2021DeepCL,
  title={Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis},
  author={Jun Luo and Dooman Arefan and Margarita L. Zuley and Jules H. Sumkin and Shandong Wu},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11320}
}
Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succeeded in reaching near-human performance in a number of tasks, and its application in mammography is one of the topics that medical researchers most concentrate on. 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… 

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References

SHOWING 1-8 OF 8 REFERENCES
Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images
TLDR
This work proposes a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework and proposes an algorithm that updates the sampling probabilities at each epoch, applicable to other sampling-based curriculum learning frameworks.
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Coarse-to-Fine Curriculum Learning for Classification
  • [International Conference on Learning Representations (ICLR) Workshop on Bridging AI and Cognitive Science (BAICS) ], (2020).
  • 2020
Medical-based Deep Curriculum Learning for Improved Fracture Classification
TLDR
This work proposes and compares several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement.
Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening
TLDR
This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls.
Curriculum learning
TLDR
It is hypothesized that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions).
Breast cancer statistics
TLDR
Breast cancer rates vary largely by race/ethnicity and socioeconomic status (SES), and geographic region, and death rates are higher in African American women than in whites, despite their lower incidence rates.
U . S . Breast Cancer Statistics