Deep learning of feature representation with multiple instance learning for medical image analysis

@article{Xu2014DeepLO,
  title={Deep learning of feature representation with multiple instance learning for medical image analysis},
  author={Yan Xu and Tao Mo and Qiwei Feng and Peilin Zhong and Maode Lai and Eric I-Chao Chang},
  journal={2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2014},
  pages={1626-1630}
}
  • Yan XuTao Mo E. Chang
  • Published 4 May 2014
  • Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
This paper studies the effectiveness of accomplishing high-level tasks with a minimum of manual annotation and good feature representations for medical images. [] Key Method We use multiple instance learning (MIL) framework in classification training with deep learning features. Several interesting conclusions can be drawn from our work: (1) automatic feature learning outperforms manual feature; (2) the unsupervised approach can achieve performance that's close to fully supervised approach (93.56%) vs. (94…
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References

SHOWING 1-10 OF 26 REFERENCES

Contexts-Constrained Multiple Instance Learning for Histopathology Image Analysis

A new algorithm along the line of weakly supervised learning is proposed; context constraints as a prior for multiple instance learning (ccMIL) is introduced, which significantly reduces the ambiguity in weak supervision (a 20% gain).

Learning invariant features of tumor signatures

A novel method for automated learning of features from unlabeled image patches for classification of tumor architecture that outperforms more complex expert-designed approaches and promises a wider application of self-learning strategies for tissue characterization.

An Analysis of Single-Layer Networks in Unsupervised Feature Learning

The results show that large numbers of hidden nodes and dense feature extraction are critical to achieving high performance—so critical, in fact, that when these parameters are pushed to their limits, they achieve state-of-the-art performance on both CIFAR-10 and NORB using only a single layer of features.

Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives

Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, manifold learning, anddeep learning.

Sparse Feature Learning for Deep Belief Networks

This work proposes a simple criterion to compare and select different unsupervised machines based on the trade-off between the reconstruction error and the information content of the representation, and describes a novel and efficient algorithm to learn sparse representations.

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

The convolutional deep belief network is presented, a hierarchical generative model which scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.

Learning invariant features through topographic filter maps

This work proposes a method that automatically learns feature extractors in an unsupervised fashion by simultaneously learning the filters and the pooling units that combine multiple filter outputs together.

ImageNet classification with deep convolutional neural networks

A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.

What is the best multi-stage architecture for object recognition?

It is shown that using non-linearities that include rectification and local contrast normalization is the single most important ingredient for good accuracy on object recognition benchmarks and that two stages of feature extraction yield better accuracy than one.

Discriminative learned dictionaries for local image analysis

This article proposes an energy formulation with both sparse reconstruction and class discrimination components, jointly optimized during dictionary learning, for local image discrimination tasks, and paves the way for a novel scene analysis and recognition framework based on simultaneously learning discriminative and reconstructive dictionaries.