Machine Learning in Medical Imaging Before and After Introduction of Deep Learning

@inproceedings{Suzuki2017MachineLI,
  title={Machine Learning in Medical Imaging Before and After Introduction of Deep Learning},
  author={Kenji Suzuki},
  year={2017}
}

Research on Big Data Classification Technology Based on Deep Learning

TLDR
This paper studies the application of deep learning in industrial big data time series classification, and analyzes the characteristics of industrial data and the challenges of industrial time series Classification from three aspects: accurate classification, efficient classification and incremental learning.

The detection of lung cancer using massive artificial neural network based on soft tissue technique

TLDR
A proposed CAD scheme without soft tissue technique attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and specificity.

The detection of lung cancer using MTANN (Massive Training Artificial Neural Network) based soft tissue technique

TLDR
A proposed computer aided detection (CAD) system using soft tissue technique to determine sensitivity in support of subtle nodules attained tremendously minimum false positive rate and it is a promising technique insupport of cancerous recognition.

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

TLDR
Two types of input methods for the classification of images are tested to provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data and the accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images.

A deep CNN based transfer learning method for false positive reduction

TLDR
A deep Convolutional Neural Network (CNN) based transfer learning method for FP reduction in pulmonary nodule detection on CT slices and results show that the overall sensitivity of the proposed method was 87.2% with 0.39 FPs per scan, which is higher than other state of art method.

References

SHOWING 1-10 OF 81 REFERENCES

Introduction to Statistical Pattern Recognition

TLDR
This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.

Edge detection from noisy images using a neural edge detector

  • Kenji SuzukiI. HoribaN. Sugie
  • Engineering
    Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501)
  • 2000
TLDR
A new edge detector using a multilayer neural network, called a neural edge detector (NED), is proposed for detecting the desired edges clearly from noisy images and the performance of the NED is the highest in terms of similarity to the wanted edges.

One-shot dual-energy subtraction imaging.

Dual-energy subtraction imaging by a single x-ray exposure (one shot) can easily be performed by using computed radiography with scanning laser-stimulated luminescence. In a phantom study, a thin

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.

TLDR
An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs) and results indicate that the method may be useful in the computer-aided detection ofmonary nodules using PET/ CT images.

Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect.

TLDR
Potentially resectable NSCLC lesions missed at chest radiography were characterized by predominantly peripheral and upper lobe locations and by apical and posterior segmental/subsegmental locations in an upper lobe.

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network

TLDR
A computer‐aided diagnosis (CADx) method to discriminate cysts from solid lesion in mammography using features from a deep CNN trained on a large set of mass candidates, obtaining an AUC of 0.80 on a set of diagnostic exams recalled from screening.

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees.

TLDR
The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study.

Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network.

TLDR
This study applies a shift-invariant neural network to eliminate false-positive detections reported by the CAD scheme, considerably better than that obtained in the previous study using a conventional three-layer, feed-forward neural network.

Characterization of mammographic masses based on level set segmentation with new image features and patient information.

TLDR
An automated method for mammographic mass segmentation is developed and new image based features in combination with patient information are explored in order to improve the performance of mass characterization.
...