AIMC Topic: Image Processing, Computer-Assisted

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Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning.

NeuroImage
Reconstructing perceived stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding. However, the inconsistent distribution and representation between fMRI signal...

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Theranostics
Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been implemented for different clinical applications. Accurate diagnosis and treatment require reliable an...

Quantitative analysis of blood cells from microscopic images using convolutional neural network.

Medical & biological engineering & computing
Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the presence of infection, inflammation, edema, bleeding, and other blood-related issues. Current microscop...

A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composi...

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

NeuroImage
Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase ...

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology.

Toxicologic pathology
Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed...

Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases.

International journal of cancer
Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pa...

Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we ...

Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning.

IEEE transactions on medical imaging
Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation o...

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.

IEEE transactions on medical imaging
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are und...