AIMC Topic: Image Processing, Computer-Assisted

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AutoLNMNet: Automated Network for Estimating Lymph-Node Metastasis in EGC Using a Pyramid Vision Transformer and Data Derived From Multiphoton Microscopy.

Microscopy research and technique
Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the mainstream treatment for EGC. However, it is challenging for even experienced endoscopists to accurately...

Adaptive wavelet-VNet for single-sample test time adaptation in medical image segmentation.

Medical physics
BACKGROUND: In medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning-based segmentation models. Given the hi...

Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.

PloS one
PURPOSE: Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetr...

Automated analysis of a novel object recognition test in mice using image processing and machine learning.

Behavioural brain research
The novel object recognition test (NORT) is one of the most commonly employed behavioral tests in experimental animals designed to evaluate an animal's interest in and recognition of novelty. However, manual procedures, which rely on researchers' obs...

PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.

Medical image analysis
Breast cancer is a significant global public health concern, with various treatment options available based on tumor characteristics. Pathological examination of excision specimens after surgery provides essential information for treatment decisions....

Reconstructing and analyzing the invariances of low-dose CT image denoising networks.

Medical physics
BACKGROUND: Deep learning-based methods led to significant advancements in many areas of medical imaging, most of which are concerned with the reduction of artifacts caused by motion, scatter, or noise. However, with most neural networks being black ...

Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques.

Microscopy research and technique
Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon canc...

Neural Memory State Space Models for Medical Image Segmentation.

International journal of neural systems
With the rapid advancement of deep learning, computer-aided diagnosis and treatment have become crucial in medicine. UNet is a widely used architecture for medical image segmentation, and various methods for improving UNet have been extensively explo...

Enhancing pap smear image classification: integrating transfer learning and attention mechanisms for improved detection of cervical abnormalities.

Biomedical physics & engineering express
Cervical cancer remains a major global health challenge, accounting for significant morbidity and mortality among women. Early detection through screening, such as Pap smear tests, is crucial for effective treatment and improved patient outcomes. How...