AIMC Topic: Image Processing, Computer-Assisted

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Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.

Physical and engineering sciences in medicine
Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundarie...

Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
The scarcity of medical resources and personnel has worsened due to COVID-19. Telemedicine faces challenges in assessing wounds without physical examination. Evaluating pressure injuries is time consuming, energy intensive, and inconsistent. Most of...

[Validation of Optimal Imaging Conditions for Coronary Computed Tomography Angiography Using High-definition Mode and Deep Learning Image Reconstruction Algorithm].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination.

Hierarchical medical image report adversarial generation with hybrid discriminator.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVES: Generating coherent reports from medical images is an important task for reducing doctors' workload. Unlike traditional image captioning tasks, the task of medical image report generation faces more challenges. Current mode...

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks.

Physics in medicine and biology
. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed probl...

Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.

Medical physics
BACKGROUND: In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challengi...

An Analysis of Loss Functions for Heavily Imbalanced Lesion Segmentation.

Sensors (Basel, Switzerland)
Heavily imbalanced datasets are common in lesion segmentation. Specifically, the lesions usually comprise less than 5% of the whole image volume when dealing with brain MRI. A common solution when training with a limited dataset is the use of specifi...

Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images.

Journal of imaging informatics in medicine
This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion d...

Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Microscopy research and technique
Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of pro...

Infrared thermal images using PCSAN-Net-DBOA: An approach of breast cancer classification.

Microscopy research and technique
This manuscript proposes thermal images using PCSAN-Net-DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR-IR) dataset for breast cancer classification. The adaptive distorted Gaussian match...