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Soft Activation Mapping of Lung Nodules in Low-Dose CT images [article]

Yiming Lei, Yukun Tian, Hongming Shan, Junping Zhang, Ge Wang, Mannudeep Kalra
2018 arXiv   pre-print
As a popular deep learning model, the convolutional neural network (CNN) has produced promising results in analyzing lung nodules and tumors in low-dose CT images.  ...  To address this challenge, in this paper we develop a soft activation mapping (SAM) to enable fine-grained lesion analysis with a CNN so that it can access rich radiomics features.  ...  Early detection of lung cancer with low dose CT (LDCT) has been recently approved in some countries including the United States to improve patient survival.  ... 
arXiv:1810.12494v1 fatcat:2niam6qbrnhpboarn7a2shc5ly

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
Traditional approaches generally leverage the information from natural images via transfer learning.  ...  Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.  ...  In addition, for the benign-malignant risk assessment of lung nodules in low-dose CT scans [71] , the binary labels about the presence of six high-level nodule attributes, namely the calcification, sphericity  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  We analyse and discuss 163 papers that apply adversarial training techniques in the context of cancer imaging and elaborate their methodologies, advantages and limitations.  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Diagnostic imaging of lung cancer

N. Hollings, P. Shaw
2002 European Respiratory Journal  
It is on this background that the radiologist remains actively employed in the detection, diagnosis, staging and review of this common malignancy.  ...  This compares badly with breast (11% reduction) and melanoma (32%). The overall 5-yr survival for lung cancer diagnosed between 1986-1990 was only 5.3% (against 66% for breast and 76% for melanoma).  ...  , controlled trials of 40,000 and 88,000 patients respectively using low-dose CT.  ... 
doi:10.1183/09031936.02.00280002 pmid:11999004 fatcat:mv5dlnnimjaotbudbu3jhivphy

Artificial intelligence in cancer imaging: Clinical challenges and applications

Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath, Maryellen L. Giger, Nicolai J. Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F. Dunn, Raymond H. Mak, Rulla M. Tamimi (+7 others)
2019 Ca  
Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems  ...  AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent  ...  Acknowledgments: We thank Ken Chang for generating the activation heatmaps in Figure 5 .  ... 
doi:10.3322/caac.21552 pmid:30720861 pmcid:PMC6403009 fatcat:czsiirkbm5f7fh2ueesgnqtlhi

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images [article]

Qinghao Ye, Yuan Gao, Weiping Ding, Zhangming Niu, Chengjia Wang, Yinghui Jiang, Minhao Wang, Evandro Fei Fang, Wade Menpes-Smith, Jun Xia, Guang Yang
2021 arXiv   pre-print
In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new  ...  However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image  ...  To- gashi, Computer-aided diagnosis of lung nodule classification between be- nign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional  ... 
arXiv:2112.04984v1 fatcat:spnk3ztuevcavgaje6acjp4ula

Proceedings of the International Cancer Imaging Society (ICIS) 17th Annual Teaching Course

2017 Cancer Imaging  
There is a wide spectrum of low-dose CT findings in lung cancer screenings.  ...  The typical presentation of early stage lung cancers on low-dose CT screening is non-calcified pulmonary nodules.  ...  Two of these patients demonstrated post-operative nystagmus and ipsilateral dysmetria. Symptoms resolved in all 4 patients within 4 weeks of surgery. All 4  ... 
doi:10.1186/s40644-017-0126-4 fatcat:fnvtnd5xgjdgnkdlnnbmsbhiuu

The Proceedings of the 19th International Cancer Imaging Society Meeting and Annual Teaching Course

2019 Cancer Imaging  
In the American NLST trial, annual low-dose chest CT in long-term smokers reduced lung cancer mortality by 20% compared to annual chest radiography.  ...  The IASLC Lung Cancer Staging Project: Background Data and Proposals for the Classification of Lung Cancer with Separate Tumour Nodules in the Forthcoming Eighth Edition of the TNM Classification for  ...  ADC maps and PET images of the pelvis were re-sliced and re-oriented with a specific software (PMOD) in order to perfectly match to the T2w images.  ... 
doi:10.1186/s40644-019-0244-2 fatcat:24addaecrngdvc2c6v37ai5qeq

Proceedings of the International Cancer Imaging Society (ICIS) 18th Annual Teaching Course

2018 Cancer Imaging  
Low-dose CT screening has been shown to significantly reduce mortality but suffers from false positive rates as high as 58%.  ...  Conclusion: Anatomically based High Resolution MRI staging for low rectal cancer is an excellent tool to reduced margin positive and to improve outcome in patients with low rectal cancer.  ...  By computing 79 radiomic features inside each 5 mm sphere location, 79 feature maps were estimated per image (316 per patient). The mean values within the maps were used for the classification task.  ... 
doi:10.1186/s40644-018-0160-x fatcat:l3fdy44aunejnfamsnka5hfiwu

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year.  ...  Acknowledgments The authors would like to thank members of the Diagnostic Image Analysis Group for discussions and suggestions.  ... 
doi:10.1016/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS.  ...  Despite its safety and efficacy, QUS suffers from several major drawbacks: poor imaging quality, inter- and intra-observer variability which hampers the reproducibility of measurements.  ...  For example, segmentation of prostate will be beneficial for clinicians to quantify the volume of the prostate gland so as to plan treatment in High-Dose-Rate (HDR) and Low-Dose-Rate (LDR) brachytherapy  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

Imaging of Pancreatic Neuroendocrine Neoplasms

Giuditta Chiti, Giulia Grazzini, Diletta Cozzi, Ginevra Danti, Benedetta Matteuzzi, Vincenza Granata, Silvia Pradella, Laura Recchia, Luca Brunese, Vittorio Miele
2021 International Journal of Environmental Research and Public Health  
PanNENs can be functioning or non-functioning in accordance with their ability or not to produce metabolically active hormones.  ...  Morphologic imaging with ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) is used for initial evaluation and staging of disease, as well as surveillance and therapy monitoring  ...  CT images (a-c) show a hyper-enhancing pancreatic nodule in the body/tail of the organ (arrows) well depicted on the coronal (d) and sagittal (e) reconstruction too.  ... 
doi:10.3390/ijerph18178895 pmid:34501485 fatcat:e25wx7shwneefoq6zc3xyixbtq

Friday 5 September 2014

2014 Journal of Medical Radiation Sciences  
of the lung using FFF.  ...  And, via multivariate regression, outline positive predictive factors for survival, to help guide patients and clinicians in the management of recurrent high-grade gliomas.  ...  Findings lend support to the use of cervical spine CT as the primary imaging modality in cervical spine clearance. CT dose optimisation: Do quality improvement activities work?  ... 
doi:10.1002/jmrs.71 pmid:27759953 pmcid:PMC4263484 fatcat:w35p7ptmcralzmtv45vyzujaoq

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
, Session 2 Estimating lesion volume in low-dose chest CT: How low can we go?  ...  Further, the phantoms allow for performance evaluation for several important imaging tasks such as low-contrast lesion detectability and lung nodule volumetric assessment.  ...  2 cm) in tens of minutes yielding images containing millions of spectra. Spectra are then automatically classified as one of seven cell-types in prostate tissue in a matter of seconds.  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
image segmentation to address the automation in identification and detection of the target regions or sub-regions.  ...  Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this  ...  Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary  ... 
arXiv:2107.04537v4 fatcat:m5oqea5q6vhbhkerjmejder3hu
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