Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation
Sign Up to like & getrecommendations! Published in 2021 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2021.106363
Abstract: BACKGROUND AND OBJECTIVE Computer-aided diagnosis (CAD) systems promote accurate diagnosis and reduce the burden of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary… read more here.
Keywords: radiology; malignancy; nodule malignancy; malignancy evaluation ... See more keywords
MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Medical image analysis"
DOI: 10.1016/j.media.2020.101772
Abstract: The accurate identification of malignant lung nodules using computed tomography (CT) screening images is vital for the early detection of lung cancer. It also offers patients the best chance of cure, because non-invasive CT imaging has… read more here.
Keywords: nodule malignancy; mscs deepln; lung nodule; lung ... See more keywords
Predicting thyroid nodule malignancy at several prevalence values with a combined Bethesda‐molecular test
Sign Up to like & getrecommendations! Published in 2017 at "Translational Research"
DOI: 10.1016/j.trsl.2017.07.005
Abstract: &NA; Investigation of thyroid nodules using fine‐needle aspiration cytology (FNAC) gives indeterminate results in up to 30% of samples using the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). We present a combined Bethesda‐molecular predictor of… read more here.
Keywords: prevalence; malignancy; thyroid nodule; predictor ... See more keywords
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on medical imaging"
DOI: 10.1109/tmi.2022.3149344
Abstract: Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come from the ambiguous labels provided by annotators, since… read more here.
Keywords: nodule malignancy; malignancy prediction; set set; prediction ... See more keywords
A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Oncology"
DOI: 10.3389/fonc.2021.737368
Abstract: Objectives Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given… read more here.
Keywords: deep learning; feature; malignancy; nodule malignancy ... See more keywords