AIMC Topic: Image Processing, Computer-Assisted

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Fundus image segmentation via hierarchical feature learning.

Computers in biology and medicine
Fundus Image Segmentation (FIS) is an essential procedure for the automated diagnosis of ophthalmic diseases. Recently, deep fully convolutional networks have been widely used for FIS with state-of-the-art performance. The representative deep model i...

A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision.

Vision research
Here we examine the plausibility of deep convolutional neural networks (CNNs) as a theoretical framework for understanding biological vision in the context of image classification. Recent work on object recognition in human vision has shown that both...

Annotation-efficient deep learning for automatic medical image segmentation.

Nature communications
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to...

SMBFT: A Modified Fuzzy -Means Algorithm for Superpixel Generation.

Computational and mathematical methods in medicine
Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classifi...

A bioinspired neural architecture search based convolutional neural network for breast cancer detection using histopathology images.

Scientific reports
The design of neural architecture to address the challenge of detecting abnormalities in histopathology images can leverage the gains made in the field of neural architecture search (NAS). The NAS model consists of a search space, search strategy and...

MedFuseNet: An attention-based multimodal deep learning model for visual question answering in the medical domain.

Scientific reports
Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the di...

Recovering SWI-filtered phase data using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs.

DSAL: Deeply Supervised Active Learning From Strong and Weak Labelers for Biomedical Image Segmentation.

IEEE journal of biomedical and health informatics
Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Things (IoMT) domain. However, annotating biomedical images is knowledge-...

A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification.

IEEE journal of biomedical and health informatics
Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high re...

The Evolving Role of Artificial Intelligence in Cardiac Image Analysis.

The Canadian journal of cardiology
Research in artificial intelligence (AI) has progressed over the past decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection a...