AIMC Topic: Carcinoma, Bronchogenic

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Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.

BMC medical imaging Jul 14, 2021
BACKGROUND: In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion spe...
Humans Neural Networks, Computer Brain Brain Neoplasms Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Glioma Magnetic Resonance Imaging Alzheimer Disease Tomography, Emission-Computed, Single-Photon Huntington Disease Carcinoma, Bronchogenic
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Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

BMC medical imaging Apr 13, 2021
BACKGROUND: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for auto...
Humans Neural Networks, Computer Tomography, X-Ray Computed Female Aged Adult Male Middle Aged Deep Learning Lung Neoplasms Lymph Nodes Lymphatic Metastasis Contrast Media Axilla Datasets as Topic Thorax Mediastinum Carcinoma, Bronchogenic
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Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Physics in medicine and biology Dec 13, 2019
Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. ...
Humans Electronic Health Records Deep Learning Lung Neoplasms Treatment Outcome Carcinoma, Bronchogenic
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