AIMC Topic: Image Processing, Computer-Assisted

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MSGAN: Multi-Stage Generative Adversarial Networks for Cross-Modality Domain Adaptation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for cross-modality medical images. In this work, we present a ne...

MVD-Net: Semantic Segmentation of Cataract Surgery Using Multi-View Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Semantic segmentation of surgery scenarios is a fundamental task for computer-aided surgery systems. Precise segmentation of surgical instruments and anatomies contributes to capturing accurate spatial information for tracking. However, uneven reflec...

A Comparison of Convolutional Neural Networks and Traditional Feature-Based Classification Applied to Leukaemia Image Analysis.

Studies in health technology and informatics
The accuracy of smear test image classification is a fundamental aspect in differentiating the type of leukaemia and determining the right treatment to improve the patient's chances of survival and recovery. Image Classification has lately become a v...

Research on artificial intelligence of accounting information processing based on image processing.

Mathematical biosciences and engineering : MBE
The rapid development and wide application of artificial intelligence is deeply affecting all aspects of human society. Combine artificial intelligence with the accounting industry, use computers to efficiently and automatically process accounting in...

Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images.

Studies in health technology and informatics
Self-supervised methods gain more and more attention, especially in the medical domain, where the number of labeled data is limited. They provide results on par or superior to their fully supervised competitors, yet the difference between information...

MISeval: A Metric Library for Medical Image Segmentation Evaluation.

Studies in health technology and informatics
Correct performance assessment is crucial for evaluating modern artificial intelligence algorithms in medicine like deep-learning based medical image segmentation models. However, there is no universal metric library in Python for standardized and re...

[Study on the accuracy of automatic segmentation of knee CT images based on deep learning].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy.

Framework for denoising Monte Carlo photon transport simulations using deep learning.

Journal of biomedical optics
SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resu...

Artificial intelligence in neuropathology: Current status and future perspectives.

Indian journal of pathology & microbiology
Machine learning and artificial intelligence (AI) have become a part of our daily routine. There are very few of us who are not influenced by this technology. There are a lot of misconceptions about the scope, utility, and fallacies of AI. Digital ne...

Investigation of image plane for image reconstruction of objects through diffusers via deep learning.

Journal of biomedical optics
SIGNIFICANCE: The imaging of objects hidden in light-scattering media is a vital practical task in a wide range of applications, including biological imaging. Deep-learning-based methods have been used to reconstruct images behind scattering media un...