320 research outputs found
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Early detection of pulmonary cancer is the most promising way to enhance a
patient's chance for survival. Accurate pulmonary nodule detection in computed
tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In
this paper, inspired by the successful use of deep convolutional neural
networks (DCNNs) in natural image recognition, we propose a novel pulmonary
nodule detection approach based on DCNNs. We first introduce a deconvolutional
structure to Faster Region-based Convolutional Neural Network (Faster R-CNN)
for candidate detection on axial slices. Then, a three-dimensional DCNN is
presented for the subsequent false positive reduction. Experimental results of
the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior
detection performance of the proposed approach on nodule detection(average
FROC-score of 0.891, ranking the 1st place over all submitted results).Comment: MICCAI 2017 accepte
Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans
Author's accepted manuscript.Available from 18/06/2021.acceptedVersio
In vitro effects of particulate matter associated with a wildland fire in the north-west of Italy
Wildland fires, increasing in recent decades in the Mediterranean region due to climate change, can contribute to PM levels and composition. This study aimed to investigate biological effects of PM2.5 (Ø 10 (Ø 10 and PM2.5 were measured during the fire suggesting that near and distant sites were influenced by fire pollutants. The PM10 and PM2.5 extracts induced a significant mutagenicity in all sites and the mutagenic effect was increased with respect to historical data. All extracts induced a slight increase of the estrogenic activity but a possible antagonistic activity of PM samples collected near fire was observed. No cytotoxicity or DNA damage was detected. Results confirm that fires could be relevant for human health, since they can worsen the air quality increasing PM concentrations, mutagenic and estrogenic effects
Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis
Author's accepted manuscript.Available from 28/11/2021.acceptedVersio
The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110105/1/cptclpt2008161.pd
Learning Visual Context by Comparison
Finding diseases from an X-ray image is an important yet highly challenging
task. Current methods for solving this task exploit various characteristics of
the chest X-ray image, but one of the most important characteristics is still
missing: the necessity of comparison between related regions in an image. In
this paper, we present Attend-and-Compare Module (ACM) for capturing the
difference between an object of interest and its corresponding context. We show
that explicit difference modeling can be very helpful in tasks that require
direct comparison between locations from afar. This module can be plugged into
existing deep learning models. For evaluation, we apply our module to three
chest X-ray recognition tasks and COCO object detection & segmentation tasks
and observe consistent improvements across tasks. The code is available at
https://github.com/mk-minchul/attend-and-compare.Comment: ECCV 2020 spotlight pape
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
Diagnosis of pulmonary lesions from computed tomography (CT) is important but
challenging for clinical decision making in lung cancer related diseases. Deep
learning has achieved great success in computer aided diagnosis (CADx) area for
lung cancer, whereas it suffers from label ambiguity due to the difficulty in
the radiological diagnosis. Considering that invasive pathological analysis
serves as the clinical golden standard of lung cancer diagnosis, in this study,
we solve the label ambiguity issue via a large-scale radio-pathomics dataset
containing 5,134 radiological CT images with pathologically confirmed labels,
including cancers (e.g., invasive/non-invasive adenocarcinoma, squamous
carcinoma) and non-cancer diseases (e.g., tuberculosis, hamartoma). This
retrospective dataset, named Pulmonary-RadPath, enables development and
validation of accurate deep learning systems to predict invasive pathological
labels with a non-invasive procedure, i.e., radiological CT scans. A
three-level hierarchical classification system for pulmonary lesions is
developed, which covers most diseases in cancer-related diagnosis. We explore
several techniques for hierarchical classification on this dataset, and propose
a Leaky Dense Hierarchy approach with proven effectiveness in experiments. Our
study significantly outperforms prior arts in terms of data scales (6x larger),
disease comprehensiveness and hierarchies. The promising results suggest the
potentials to facilitate precision medicine.Comment: MICCAI 2020 (Early Accepted
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
The recent progress of computing, machine learning, and especially deep
learning, for image recognition brings a meaningful effect for automatic
detection of various diseases from chest X-ray images (CXRs). Here efficiency
of lung segmentation and bone shadow exclusion techniques is demonstrated for
analysis of 2D CXRs by deep learning approach to help radiologists identify
suspicious lesions and nodules in lung cancer patients. Training and validation
was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset,
i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02),
original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset
after segmentation (dataset #04). The results demonstrate the high efficiency
and usefulness of the considered pre-processing techniques in the simplified
configuration even. The pre-processed dataset without bones (dataset #02)
demonstrates the much better accuracy and loss results in comparison to the
other pre-processed datasets after lung segmentation (datasets #02 and #03).Comment: 10 pages, 7 figures; The First International Conference on Computer
Science, Engineering and Education Applications (ICCSEEA2018)
(www.uacnconf.org/iccseea2018) (accepted
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions
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