computed tomography
PulseAugur coverage of computed tomography — every cluster mentioning computed tomography across labs, papers, and developer communities, ranked by signal.
- instance of cone beam computed tomography 90%
- instance of Cone Beam Ct 90%
- used by Segment Anything Model 80%
- instance of magnetic resonance imaging 70%
- instance of Gotit.pub 70%
- used by deep learning 70%
- instance of CatalyzeX 70%
- developed by CatalyzeX 70%
- used by ScienceCast 70%
- used by X-ray 70%
- developed by cone beam computed tomography 70%
- used by TotalSegmentator 70%
10 day(s) with sentiment data
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Developers showcase AI tools for medical imaging and API analysis
Two developers are showcasing AI tools they have built. One has created a system that uses general vision models to interpret MRI and CT scans, providing explanations of the findings. The other is exploring the relation…
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AI medical scan tool struggles with left-right errors, developer implements fix
A developer built a tool to interpret medical scans using large language models like Claude, Gemini, and Grok, but discovered a critical flaw: the models frequently confused left and right sides of the patient. This err…
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AI models for heart disease segmentation show poor out-of-distribution generalization
A new research paper evaluates the out-of-distribution (OOD) generalization capabilities of deep learning models for segmenting congenital heart disease (CHD) anatomies. The study found that in-distribution performance …
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SONAR: New Neural Operator Enhances Sparse-View CT Reconstruction
Researchers have developed SONAR, a novel Structure-Consistent Neural Operator designed for sparse-view computed tomography (CT) reconstruction. This method addresses the challenges of ill-posedness in CT scans with inc…
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AI and mobile CT integration aims to boost rural healthcare access
A new review paper explores the integration of mobile computed tomography (CT) systems, telehealth, and artificial intelligence (AI) to improve healthcare access in rural and remote areas. While mobile CT, telehealth, a…
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MedSAM-3 enhances medical image segmentation with text prompts and LLM agents
Researchers have introduced MedSAM-3, a new model designed for medical image segmentation that leverages text prompts for precise targeting of anatomical structures. By fine-tuning the Segment Anything Model (SAM) archi…
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New PyRadiomics Extension Enhances Anisotropic Medical Image Texture Analysis
Researchers have developed an enhanced version of PyRadiomics designed to accurately analyze texture features in medical imaging data acquired with anisotropic voxel spacing. This new framework accounts for varying phys…
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AI optimizes CT scan protocols for better image quality and lower radiation dose
Researchers have developed a novel framework utilizing reinforcement learning and virtual imaging trials to optimize computed tomography (CT) protocols. This method aims to enhance diagnostic image quality while minimiz…
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New PR-IMM tracking method improves object-motion representation
A new tracking method called PR-IMM has been developed, integrating a transformer-based prediction model with radar Doppler measurements to enhance nonlinear object-motion representation. This approach improves upon exi…
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New framework generates subcortical CT scan labels using MRI data
Researchers have developed a novel ensemble framework to generate subcortical segmentation labels for CT scans by transferring knowledge from existing MRI-based models. This approach addresses the scarcity of labeled CT…
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Unified AI model segments pancreas across CT and MRI scans
Researchers have developed a unified framework for segmenting pancreas images from both CT and MRI scans, addressing the challenge of performance degradation when models trained on one modality are applied to another. B…
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New 3D CT-to-PET translation uses latent diffusion models
Researchers have developed a novel 3D CT-to-PET translation framework using latent Brownian Bridge Diffusion (BBDM). This two-stage method first employs a Variational Autoencoder (VAE) with contrastive learning to align…
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New spectral adapters enhance SAM for medical image segmentation
Researchers have developed two novel spectral adapters, DiSECT and SiGA, designed to enhance the Segment Anything Model (SAM) for segmenting colorectal liver metastases (CRLM) in CT scans. These adapters aim for paramet…
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New UBone3D framework enhances 3D shape completion from ultrasound data
Researchers have developed UBone3D, a new framework designed to improve the accuracy and anatomical fidelity of 3D shape completion from ultrasound data. This method utilizes physics-rectified conditional flow matching …
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New GRIPNet architecture improves pulmonary nodule detection in CT scans
Researchers have developed GRIPNet, a novel deep learning architecture designed to improve the detection of pulmonary nodules in CT scans. Unlike previous methods that treat nodules as generic objects, GRIPNet leverages…
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AI intelligence framework places GPT-4o, Claude 3.5 in collaborator tier
A theoretical framework called "Carbon-Silicon Dao" proposes a five-tier system for classifying intelligence, from basic pattern matching to self-aware consciousness. The framework uses a ten-dimensional scale to positi…
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Federated learning boosts cross-modality medical image segmentation
A new research paper explores federated learning techniques to improve cross-modality medical image segmentation, addressing challenges posed by data distributed across institutions and varying imaging protocols. The st…
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LeCor method improves 3D lung tumor segmentation with meta-learned training
Researchers have developed LeCor, a novel method for improving 3D lung tumor segmentation in computed tomography (CT) scans. LeCor utilizes meta-learned test-time training, where each clinician's correction acts as a tr…
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LLM accurately selects chest CT protocols from clinical text
Researchers have developed a decision support system that uses a large language model (LLM) to automate the selection of chest CT protocols. This system, which leverages text embeddings from clinical imaging requests, a…
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SynthRCT framework generates synthetic 4DCT images for proton therapy robustness
Researchers have developed SynthRCT, a novel framework for generating synthetic 4D computed tomography (4DCT) images, which are crucial for evaluating the robustness of proton therapy treatment plans. This conditional g…