computed tomography
PulseAugur coverage of computed tomography — every cluster mentioning computed tomography across labs, papers, and developer communities, ranked by signal.
- used by nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation 80%
- instance of magnetic resonance imaging 70%
- used by deep learning 70%
- used by X-ray 70%
- used by polyethylene terephthalate 70%
- instance of cone beam computed tomography 70%
- used by U-Net 70%
- used by Segment Anything Model 70%
- competes with magnetic resonance imaging 60%
- instance of Mauritius 60%
- used by ultrasound 60%
- used by Mauritius 60%
18 day(s) with sentiment data
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Podcast argues trustworthy AI is a category error
A podcast episode from c't Magazin discusses the concept of trustworthy AI, arguing that it is fundamentally flawed. The discussion likely explores the inherent challenges and limitations in ensuring AI systems are reli…
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New framework enhances medical image super-resolution with dual-prior learning
Researchers have developed a new framework called Dual-Prior Null-space Learning (DP-NSL) for arbitrary slice super-resolution in medical imaging. This method reconstructs isotropic volumes from anisotropic clinical acq…
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AI predicts imatinib response in GIST using multimodal learning · 2 sources tracked
Researchers have developed a multimodal deep learning framework using cross-attention to predict patient response to neoadjuvant imatinib for gastrointestinal stromal tumors (GISTs). The model integrates computed tomogr…
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Render-FM achieves real-time photorealistic CT scan rendering
Researchers have developed Render-FM, a novel feedforward model designed for real-time photorealistic volumetric rendering of CT scans. This model significantly speeds up the rendering process, reducing it from hours or…
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New AI framework shows promise for heart chamber segmentation from CT scans
Researchers have developed ChameleonNet, a deep learning framework designed to segment heart chambers from non-contrast CT scans. This method utilizes contrastive unpaired image translation to synthesize non-contrast CT…
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New BenchX benchmark reveals AI cancer detection models struggle with diverse patient subgroups
A new benchmark called BenchX has been developed to evaluate AI models used in cancer detection and localization. This benchmark, comprising 85,355 CT scans, assesses 12 AI models for their performance across various pa…
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MorVess framework improves pulmonary vessel segmentation using geometric priors
Researchers have developed MorVess, a novel framework for segmenting pulmonary vessels in computed tomography scans. This morphology-aware approach integrates geometric priors with foundation model adaptation to improve…
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Human-AI collaboration boosts medical image segmentation accuracy
Researchers have developed Hi-Seg, a framework that enhances the Segment Anything Model (SAM) for pulmonary nodule segmentation in medical imaging. This human-in-the-loop system allows annotators, including non-medical …
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Efficient CNN with Transfer Learning Achieves High Accuracy in Multi-Cancer Detection
Researchers have developed a computationally efficient convolutional neural network (CNN) that utilizes transfer learning for multi-cancer detection from biomedical images. This lightweight model aims to reduce computat…
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New AI framework reconstructs pediatric skull CT from X-rays
Researchers have developed PSCT-Net, a novel framework for reconstructing 3D CT scans of pediatric skulls from sparse bi-planar X-rays. This method addresses the limitations of existing techniques by incorporating geome…
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Biomedical Engineering: Principles, History, and Applications
Biomedical engineering is a multidisciplinary field that applies engineering principles to medicine and biology, focusing on areas like device design, biomaterials, and medical imaging. Key principles include an interdi…
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Machine Learning in Healthcare Course Syllabus Detailed
This document outlines a comprehensive curriculum for a Machine Learning in Healthcare course. It covers fundamental concepts like the distinction between machine learning and deep learning, various neural network archi…
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New framework uses 2D models for 3D medical anomaly detection
Researchers have developed CS3F, a novel framework for training-free zero-shot anomaly detection in 3D medical images. This approach utilizes existing 2D foundation models by decomposing 3D volumes into slices and encod…
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Midjourney unveils radiation-free ultrasound body scanner and spa plans
Midjourney, known for its AI image generation tools, has unveiled a new medical imaging system called the Midjourney Scanner. This prototype device uses ultrasound technology, described as radiation-free and magnet-free…
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New research tackles VLM hallucinations, distillation, and interpretability
Researchers are developing new methods to improve the capabilities and reliability of vision-language models (VLMs). One approach, DCLA, focuses on mitigating hallucinations by ensuring consistency across different laye…
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New AI framework automates EVAR analysis from CT scans
Researchers have developed a new transformer-based framework called CEVAR for automated centerline extraction in endovascular aneurysm repair (EVAR) from CT scans. This method aims to improve the detection of post-EVAR …
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New AI Model Predicts Brain Bleed Expansion Using CT Scans
Researchers have developed HemExp, a novel latent diffusion model designed to predict hematoma expansion after spontaneous intracerebral hemorrhage. This model generates patient-specific follow-up non-contrast CT images…
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New AI framework improves trauma detection in CT scans
Researchers have developed CT-VDETR, a novel framework for detecting traumatic injuries in CT scans, addressing the challenge of limited voxel-level annotations. The system combines self-supervised pretraining using Mas…
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LUCID framework uses Flow Matching for sparse-view CT reconstruction
Researchers have introduced LUCID, a novel framework for reconstructing high-quality computed tomography (CT) images from sparse-view data. This method utilizes Flow Matching for generative modeling, enabling it to adap…
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AI advances medical image segmentation with new frameworks and techniques · 8 sources tracked
Researchers are developing advanced AI frameworks for medical image segmentation, focusing on improving accuracy and efficiency. Hi-Seg enhances the Segment Anything Model (SAM) for pulmonary nodule segmentation through…