8,599 research outputs found
Multi-scale analysis of lung computed tomography images
A computer-aided detection (CAD) system for the identification of lung
internal nodules in low-dose multi-detector helical Computed Tomography (CT)
images was developed in the framework of the MAGIC-5 project. The three modules
of our lung CAD system, a segmentation algorithm for lung internal region
identification, a multi-scale dot-enhancement filter for nodule candidate
selection and a multi-scale neural technique for false positive finding
reduction, are described. The results obtained on a dataset of low-dose and
thin-slice CT scans are shown in terms of free response receiver operating
characteristic (FROC) curves and discussed.Comment: 18 pages, 12 low-resolution figure
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches
Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance
A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations. [Abstract copyright: AJTR Copyright © 2020.
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