AIMC Topic: Image Processing, Computer-Assisted

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On the Reconstruction of Face Images from Deep Face Templates.

IEEE transactions on pattern analysis and machine intelligence
State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this ...

Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.

IEEE journal of biomedical and health informatics
Compact binary representations of histopa-thology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality ...

Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning.

PloS one
The World Health Organization (WHO) has clear guidelines regarding the use of Ki67 index in defining the proliferative rate and assigning grade for pancreatic neuroendocrine tumor (NET). WHO mandates the quantification of Ki67 index by counting at le...

Structural inference embedded adversarial networks for scene parsing.

PloS one
Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and ad...

Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.

Public health nutrition
OBJECTIVE: The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis.

KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

Magnetic resonance in medicine
PURPOSE: To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a ...

A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images.

Medical image analysis
Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the ...