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1 |
Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
شبکه یادگیری کوانتومی کم عمق کاملاً خود نظارتی الهام گرفته از Qutrit برای تقسیم بندی تومور مغزی-2022 Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due
to forceful termination. Qubits or bilevel quantum bits often
describe quantum neural network models. In this article, a novel
self-supervised shallow learning network model exploiting the
sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network
(QFS-Net), is presented for automated segmentation of brain
magnetic resonance (MR) images. The QFS-Net model comprises
a trinity of a layered structure of qutrits interconnected through
parametric Hadamard gates using an eight-connected secondorder neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural
network model to encode the quantum states, thereby enabling a
faster self-organized counterpropagation of these states between
the layers without supervision. The suggested QFS-Net model
is tailored and extensively validated on the Cancer Imaging
Archive (TCIA) dataset collected from the Nature repository.
The experimental results are also compared with state-of-theart supervised (U-Net and URes-Net architectures) and the selfsupervised QIS-Net model and its classical counterpart. Results
shed promising segmented outcomes in detecting tumors in terms
of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net
is also investigated on natural gray-scale images from the
Berkeley segmentation dataset and yields promising outcomes
in segmentation, thereby demonstrating the robustness of the
QFS-Net model.
Index Terms: tum computing | qutrit | U-Net and URes-Net. |
مقاله انگلیسی |
2 |
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
انجمن زیر نوع ژنومی گلیوم درجه پایین با ویژگی های شکل خودکار توسط یک الگوریتم یادگیری عمیق استخراج شده-2019 Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with
shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using
deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes.
We used preoperative imaging and genomic data of 110 patients from 5 institutions with lower-grade gliomas
from The Cancer Genome Atlas. Based on automatic deep learning segmentations, we extracted three features
which quantify two-dimensional and three-dimensional characteristics of the tumors. Genomic data for the
analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/
19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. To analyze
the relationship between the imaging features and genomic clusters, we conducted the Fisher exact test for 10
hypotheses for each pair of imaging feature and genomic subtype. To account for multiple hypothesis testing, we
applied a Bonferroni correction. P-values lower than 0.005 were considered statistically significant.
We found the strongest association between RNASeq clusters and the bounding ellipsoid volume ratio
(p < 0.0002) and between RNASeq clusters and margin fluctuation (p < 0.005). In addition, we identified
associations between bounding ellipsoid volume ratio and all tested molecular subtypes (p < 0.02) as well as
between angular standard deviation and RNASeq cluster (p < 0.02). In terms of automatic tumor segmentation
that was used to generate the quantitative image characteristics, our deep learning algorithm achieved a mean
Dice coefficient of 82% which is comparable to human performance. Keywords: Deep learning | Brain segmentation | Radiogenomics | MRI | LGG |
مقاله انگلیسی |
3 |
Deep learning for variational multimodality tumor segmentation in PET/CT
یادگیری عمیق برای تقسیم تومور چند متغیری تغییرات در PET / CT-2019 Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire func- tional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality infor- mation for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the prob- ability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the prob- ability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: (1) Only a few training samples were needed for training the designed network to produce the probability map; (2) The proposed method can be ap- plied to small datasets, normally seen in clinic research; (3) The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multi- modality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); (4) The proposed method had a good performance for tumor segmen- tation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ±0.05, sensitivity (SE) of 0.86 ±0.07, positive predictive value (PPV) of 0.87 ±0.10, volume error (VE) of 0.16 ±0.12, and classification error (CE) of 0.30 ±0.12. Keywords: Tumor segmentation | PET/CT images | Variational method | Deep learning | Information fusion |
مقاله انگلیسی |
4 |
Big data analysis for brain tumor detection: Deep convolutional neural networks
تجزیه و تحلیل داده های بزرگ برای تشخیص تومور مغزی: شبکه های عصبی پیچیده عمیق-2018 Brain tumor detection is an active area of research in brain image processing. In this work, a methodology is
proposed to segment and classify the brain tumor using magnetic resonance images (MRI).Deep Neural Networks
(DNN) based architecture is employed for tumor segmentation. In the proposed model, 07 layers are used for
classification that consist of 03 convolutional, 03 ReLU and a softmax layer. First the input MR image is divided
into multiple patches and then the center pixel value of each patch is supplied to the DNN. DNN assign labels
according to center pixels and perform segmentation. Extensive experiments are performed using eight large scale
benchmark datasets including BRATS 2012 (image dataset and synthetic dataset), 2013 (image dataset and synthetic
dataset), 2014, 2015 and ISLES (Ischemic stroke lesion segmentation) 2015 and 2017. The results are validated on
accuracy (ACC), sensitivity (SE), specificity (SP), Dice Similarity Coefficient (DSC), precision, false positive rate
(FPR), true positive rate (TPR) and Jaccard similarity index (JSI) respectively.
Keywords: Random Forests; Segmentation; Patches; Filters; Tissues |
مقاله انگلیسی |
5 |
قطعهبندی تومورهای مغزی با استفاده از شبکههای عصبی کانولوشن در تصاویر ام آر آی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 40 در بین تومورهای مغزی، غدهها شایعترین و تهاجمیترین نوع آن ها هستند که در بالاترین درجات به کاهش زیاد متوسط عمر منجر میشوند. بدین سبب، برنامهریزی درمانی، مرحله مهمی در بهبود کیفیت زندگی بیماران انکولوژی به شمار میرود. تصویربرداری با تشدید مغناطیس (ام آر آی) پرکابردترین روش تصویربرداری برای ارزیابی اینگونه تومورها میباشد، با اینهمه حجم زیاد دادههای تولیدی ام آر آی مانع قطعهبندی دستی در زمان مقتضی شده و استفاده از اندازهگیریهای کمی دقیق در کار بالینی را محدود میکند. با این حال، تغییرپذیری زیاد ساختاری و فضایی میان تومورهای مغزی مسئله قطعه بندی خودکار را با مشکل مواجه میکند. در این مقاله، روش قطعهبندی خودکار مبتنی بر شبکههای عصبی کانولوشن (CNN) جهت کاوش هستههای کوچک 3 × 3 ارائه میدهیم. استفاده از هستههای کوچک علاوه بر تأثیرگذاری مثبت در برابر تطابق بیش ار حد، امکان طراحی یک ساختار عمیقتر را فراهم نموده و اوزان کمتری را در شبکه نشان میدهد. ما همچنین استفاده از عادیسازی شدت را با وجود عمومیت آن در روشهای قطعهبندی مبتنی بر شبکه عصبی کانولوشن به عنوان مرحله پیشپردازش بررسی نموده و اثبات کردیم که به همراه افزایش دادهها میتواند در قطعهبندی تصاویر ام آر آی تومورهای مغزی بسیار کارآمد باشد. طرح پیشنهادی ما مورد تأیید پایگاه دادهای Challenge BRATS 2013 جهت قطعهبندی تومورهای مغزی قرار گرفت و همزمان در نواحی کامل، هسته و افزایشی در متریکهای ضریب شباهت دایس (88/0، 83/0، 77/0) مقام اول را در پایگاه دادهای Challenge بدست آورد. این طرح در پایگاه ارزیابی برخط نیز در کل مقام اول را کسب کرد. ما همچنین با همان مدل در پایگاه Challenge در محل BRATS 2015 شرکت کردیم و توانستیم به کمک متریک ضریب شباهت دایس با مقادیر 78/0، 65/0 و 75/0 به ترتیب در نواحی کامل، هسته و افزایشی به مقام دوم دست یابیم.
عبارات شاخص: تومور مغزی | قطعهبندی تومور مغزی | شبکههای عصبی کانولوشن | یادگیری عمیق | غده | تصویربرداری با تشدید مغناطیس |
مقاله ترجمه شده |