AIMC Topic: Lung Neoplasms
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Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Sep 20, 2022
According to the limited molecular information reflected by single spectroscopy, and the complementarity of FTIR spectroscopy and Raman spectroscopy, we propose a novel diagnostic technology combining multispectral fusion and deep learning. We used s...
Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.
Computer methods and programs in biomedicine
Sep 20, 2022
BACKGROUND AND OBJECTIVE: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, ...
Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.
Japanese journal of radiology
Sep 19, 2022
PURPOSE: To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data char...
ASO Author Reflections: The Clinical Use of Radiomics with Artificial Intelligence in Patients with Early-Stage Lung Cancer.
Annals of surgical oncology
Sep 13, 2022
Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.
Computational intelligence and neuroscience
Sep 13, 2022
The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients' courses of treatment, has become increasi...
A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.
Histopathology
Sep 12, 2022
AIMS: The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artif...
Number of lymph nodes dissected and upstaging rate of the N factor in robot-assisted thoracic surgery versus video-assisted thoracic surgery for patients with cN0 primary lung cancer.
Surgery today
Sep 9, 2022
PURPOSE: The accuracy of lymph node (LN) dissection in robotic surgery for lung cancer remains controversial. We compared the accuracy of LN dissection in robot-assisted thoracic surgery (RATS) vs. video-assisted thoracic surgery (VATS).
Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques.
Computer methods and programs in biomedicine
Sep 7, 2022
BACKGROUND: Lung cancer has the highest mortality rate in the world, twice as high as the second highest. On the other hand, pathologists are overworked and this is detrimental to the time spent on each patient, diagnostic turnaround time, and their ...
Direct identification of ALK and ROS1 fusions in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning algorithms.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Sep 3, 2022
Anaplastic lymphoma kinase (ALK) and ROS oncogene 1 (ROS1) gene fusions are well-established key players in non-small cell lung cancer (NSCLC). Although their frequency is relatively low, their detection is important for patient care and guides thera...
Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.
BMC medical informatics and decision making
Sep 1, 2022
BACKGROUND: Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting primary lung cancer sites an...
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