AIMC Topic: Image Processing, Computer-Assisted

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The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: A proof-of-principle investigation.

Mutation research. Genetic toxicology and environmental mutagenesis
The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of th...

A conditional Triplet loss for few-shot learning and its application to image co-segmentation.

Neural networks : the official journal of the International Neural Network Society
Few-shot learning tries to solve the problems that suffer the limited number of samples. In this paper we present a novel conditional Triplet loss for solving few-shot problems using deep metric learning. While the conventional Triplet loss suffers t...

Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network.

BioMed research international
Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classifi...

Bidirectional Interaction Network for Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Person re-identification (ReID) task aims to retrieve the same person across multiple spatially disjoint camera views. Due to huge image changes caused by various factors such as posture variation and illumination transformation, images of different ...

Uncertainty Class Activation Map (U-CAM) Using Gradient Certainty Method.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering t...

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping.

Evaluation of transfer learning in deep convolutional neural network models for cardiac short axis slice classification.

Scientific reports
In computer-aided analysis of cardiac MRI data, segmentations of the left ventricle (LV) and myocardium are performed to quantify LV ejection fraction and LV mass, and they are performed after the identification of a short axis slice coverage, where ...

Deep learning-Based 3D inpainting of brain MR images.

Scientific reports
The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The p...

MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.

BMC medical imaging
BACKGROUND: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medi...

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.

eLife
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocess...