AIMC Topic: Tomography, X-Ray Computed
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CQformer: Learning Dynamics Across Slices in Medical Image Segmentation.
IEEE transactions on medical imaging
Feb 4, 2025
Prevalent studies on deep learning-based 3D medical image segmentation capture the continuous variation across 2D slices mainly via convolution, Transformer, inter-slice interaction, and time series models. In this work, via modeling this variation b...
Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Feb 3, 2025
BACKGROUND: Adjuvant radiotherapy is essential for reducing local recurrence and improving survival in breast cancer patients, but it carries a risk of ischemic cardiac toxicity, which increases with heart exposure. The isocentric lateral decubitus p...
Machine Learning-Based CT Radiomics Model to Predict the Risk of Hip Fragility Fracture.
Academic radiology
Feb 3, 2025
RATIONALE AND OBJECTIVES: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.
Multi-modal dataset creation for federated learning with DICOM-structured reports.
International journal of computer assisted radiology and surgery
Feb 3, 2025
Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging...
Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer.
BMC medical imaging
Feb 3, 2025
Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of li...
Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.
Journal of cancer research and clinical oncology
Feb 3, 2025
OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to...
Optimization of sparse-view CT reconstruction based on convolutional neural network.
Medical physics
Feb 2, 2025
BACKGROUND: Sparse-view CT shortens scan time and reduces radiation dose but results in severe streak artifacts due to insufficient sampling data. Deep learning methods can now suppress these artifacts and improve image quality in sparse-view CT reco...
Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Feb 2, 2025
PURPOSE: This study aimed to develop a deep-learning framework to generate multi-organ masks from CT images in adult and pediatric patients.
SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging.
Neural networks : the official journal of the International Neural Network Society
Feb 1, 2025
Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion class...
Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.
International journal of surgery (London, England)
Feb 1, 2025
BACKGROUND: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggressive biological characteristics and a dismal prognosis. Predicting early recurrence may help determine treatment strategies for LAGC. The goal is to d...
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