AIMC Topic: Image Processing, Computer-Assisted

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Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation.

Scientific reports
Brain tumour identification, segmentation cataloguing from MRI images is most thought-provoking and is a very much essential for many medical image analysis applications. Every brain imaging modality provides information about various parts of the tu...

Insertion of hepatic lesions into clinical photon-counting-detector CT projection data.

Physics in medicine and biology
To facilitate task-driven image quality assessment of lesion detectability in clinical photon-counting-detector CT (PCD-CT), it is desired to have patient image data with known pathology and precise annotation. Standard patient case collection and re...

Exploiting deep transfer learning based precise classification and grading of renal cell carcinoma using histopathological images.

Scientific reports
Renal cancer is a key reason for cancer-related deaths among males worldwide. Earlier diagnosis of renal cancer is critical since it can considerably increase the chance of survivability. However evaluating the histopathological renal tissue is a ted...

Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.

Nature communications
Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which r...

Enhanced retinal blood vessel segmentation via loss balancing in dense generative adversarial networks with quick attention mechanisms.

International ophthalmology
PURPOSE: Manual segmentation of retinal blood vessels in fundus images has been widely used for detecting vascular occlusion, diabetic retinopathy, and other retinal conditions. However, existing automated methods face challenges in accurately segmen...

Histopathological cancer images classification with Deng entropy.

Biomedical physics & engineering express
Histopathological imaging is of paramount importance for the initial detection, diagnosis, and classification of tumors. Recurrent neural networks and convolutional neural networks have led to substantial advancements in digital pathology, thereby en...

Effective image compression using transformer and residual network for balanced handling of high and low-frequency information.

PloS one
Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. Howev...

Diffusion-based skin disease data augmentation with fine-grained detail preservation and interpolation for data diversity.

PloS one
We propose a data augmentation technique utilizing a Diffusion-based generative deep learning model to address the issue of data scarcity in skin disease diagnosis research. Specifically, we enhanced the Stable Diffusion model, a Latent Diffusion Mod...

Centiloid values from deep learning-based CT parcellation: a valid alternative to freesurfer.

Alzheimer's research & therapy
BACKGROUND: Amyloid PET/CT is essential for quantifying amyloid-beta (Aβ) deposition in Alzheimer's disease (AD), with the Centiloid (CL) scale standardizing measurements across imaging centers. However, MRI-based CL pipelines face challenges: high c...

Enhanced image registration based brain tumour segmentation using optical particle swarm intelligence technique with Resnet Inceptionv2 HCNN.

Scientific reports
A brain tumor is the deadliest disease to cause sudden death, affecting billions of people worldwide. Artificial Intelligence (AI) powered technologies play a vital role in screening medical images to identify brain-suspecting tissue regions of attai...