AIMC Topic: Image Interpretation, Computer-Assisted
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Application-driven validation of posteriors in inverse problems.
Medical image analysis
Jan 23, 2025
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models and Invertible...
Dynamic graph based weakly supervised deep hashing for whole slide image classification and retrieval.
Medical image analysis
Jan 23, 2025
Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular...
A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.
BMC medical imaging
Jan 23, 2025
Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the effic...
Automated vs manual cardiac MRI planning: a single-center prospective evaluation of reliability and scan times.
European radiology
Jan 22, 2025
OBJECTIVES: Evaluating the impact of an AI-based automated cardiac MRI (CMR) planning software on procedure errors and scan times compared to manual planning alone.
Leveraging labelled data knowledge: A cooperative rectification learning network for semi-supervised 3D medical image segmentation.
Medical image analysis
Jan 22, 2025
Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabe...
Multiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography.
Medical image analysis
Jan 22, 2025
Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. C...
DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation.
Biomedical physics & engineering express
Jan 22, 2025
In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped n...
CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.
Neuroinformatics
Jan 22, 2025
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existin...
Improved Image Quality Through Deep Learning Acceleration of Gradient-Echo Acquisitions in Uterine MRI: First Application with the Female Pelvis.
Academic radiology
Jan 21, 2025
RATIONALE AND OBJECTIVES: The aim of this study was to compare the image quality of a deep learning (DL)-accelerated volumetric interpolated breath-hold examination (VIBE) sequence with a standard (ST) VIBE sequence in assessing the uterus.
Towards contrast-agnostic soft segmentation of the spinal cord.
Medical image analysis
Jan 21, 2025
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi an...
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