AIMC Topic: Tomography, X-Ray Computed
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Multimodal deep learning: tumor and visceral fat impact on colorectal cancer occult peritoneal metastasis.
European radiology
Feb 17, 2025
OBJECTIVES: This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.
Label-efficient sequential model-based weakly supervised intracranial hemorrhage segmentation in low-data non-contrast CT imaging.
Medical physics
Feb 17, 2025
BACKGROUND: In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) in emergency stroke imaging for severity assessment. However, compared to magnetic resonance imaging (MRI), ICH shows low contrast a...
Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from .
Frontiers in public health
Feb 17, 2025
OBJECTIVE: To develop and evaluate the effectiveness of multimodal machine learning approach for the differentiation of NTM from MTB.
Machine learning model based on preoperative contrast-enhanced CT and clinical features to predict perineural invasion in gallbladder carcinoma patients.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Feb 16, 2025
BACKGROUND: Perineural invasion (PNI) is an independent prognostic risk factor for gallbladder carcinoma (GBC). However, there is currently no reliable method for the preoperative noninvasive prediction of PNI.
Prior knowledge-based multi-task learning network for pulmonary nodule classification.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 16, 2025
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...
CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy.
Academic radiology
Feb 15, 2025
RATIONALE AND OBJECTIVES: Immunotherapy combined with chemotherapy has improved outcomes for some esophageal squamous cell carcinoma (ESCC) patients, but accurate pre-treatment risk stratification remains a critical gap. This study constructed a deep...
Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.
European journal of radiology
Feb 15, 2025
PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the ...
Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning.
BMC medical informatics and decision making
Feb 15, 2025
The diagnosis of pancreatic cancer presents a significant challenge due to the asymptomatic nature of the disease and the fact that it is frequently detected at an advanced stage. This study presents a novel approach combining graph-based data repres...
CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.
European radiology
Feb 14, 2025
BACKGROUND: While the CT-based method of detecting clinically significant portal hypertension (CSPH) emerged as a noninvasive alternative for evaluating CSPH, its predictive ability for post-hepatectomy outcomes is unknown. Therefore, this study aime...
Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models.
International journal of computer assisted radiology and surgery
Feb 14, 2025
PURPOSE: Statistical shape models (SSMs) are widely used for morphological assessment of anatomical structures. However, a key limitation is the need for a clear relationship between the model's shape coefficients and clinically relevant anatomical p...
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