MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Analysis of Contingency Tables

The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health care sector has not achieved society’s expectations, even though the sector consumes a huge percentage of national budgets. Mostly, the interpretations of medical data are analyzed by medical experts. In terms of a medical expert interpreting images, this is quite limited due to its subjectivity and the complexity of the images; extensive variations exist between experts and fatigue sets in due to their heavy workload. Following the success of deep learning in other real-world applications, it is seen as also providing exciting and accurate solutions for medical imaging, and is seen as a key method for future applications in the health care sector. In this chapter, we discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification. The chapter closes with a discussion of the challenges of deep learning methods with regard to medical imaging and open research issue.

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consume huge percentage of budget. Mostly the interpretations of medical data is being done by medical expert. In terms of image interpretation by human expert, it is quite limited due to its subjectivity, the complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in other real world application, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in health secotr. In this chapter, we discussed state of the art deep learning architecture and its optimization used for medical image segmentation and classification. In the last section, we have discussed the challenges deep learning based methods for medical imaging and open research issue.

Deep Learning for Medical Image Processing: Overview, Challenges and Future

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results

/pdf/exploring-deep-learning-and-transfer-learning-for-colonic-1tlrr32prw.pdf

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Directional wavelet based features for colonic polyp classification

In this work, four well known convolutional neural networks (CNNs) that were pretrained on the ImageNet database are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. The images are classified using three different transfer learning strategies and a experimental setup specifically adapted for the classification of endoscopic imagery. The CNNs are either used as fixed feature extractors without any fine-tuning to our endoscopic celiac disease image database or they are fine-tuned by training either all layers of the CNN or by fine-tuning only the fully connected layers. Classification is performed by the CNN SoftMax classifier as well as linear support vector machines. The CNN results are compared with the results of four state-of-the-art image representations. We will show that fine-tuning all the layers of the nets achieves the best results and outperforms the comparison approaches.

CNN transfer learning for the automated diagnosis of celiac disease

Local fractal dimension based approaches for colonic polyp classification.

https://wavelab.at/papers/Hegenbart13a.pdf

Georg Wimmer

Papers

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Directional wavelet based features for colonic polyp classification

CNN transfer learning for the automated diagnosis of celiac disease

Local fractal dimension based approaches for colonic polyp classification.

Scale invariant texture descriptors for classifying celiac disease