Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy

Παπανδριανός, Νικόλαος; Papageorgiou, Elpiniki I.; Anagnostis, Athanasios

doi:10.1007/s12149-020-01510-6

Cited by 25 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to previously conducted studies and the corresponding results, CNNs seem to attain similar accuracy to that of medical experts, and the approaches developed [8,10,11]. However, it emerges that there are not any studies performing the three-class categorization into ischemia, infarction, and normal.…”

Section: Introductionmentioning

confidence: 88%

“…An automated algorithm for classifying CAD images is highly necessary for nuclear physicians since the increasing number of cases causes a bottleneck for the doctors [7]. Concerning CAD diagnosis in nuclear image analysis, ML has been introduced and investigated by various research studies conducted so far, as a methodology for automatic classification [1,[8][9][10][11][12]. Already, CAD systems have proven themselves as a highly stable method in the domain of cardiovascular data analysis, due to their ability to extract data in highly respected analyses, such as SPECT MPI.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Παπανδριανός

Feleki

Papageorgiou

et al. 2022

JCM

View full text Add to dashboard Cite

(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions in coronary artery disease. For these procedures, convolutional neural networks have proven to be very beneficial in achieving near-optimal accuracy for the automatic classification of SPECT images. (2) Methods: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD. For comparison purposes, we employ VGG-16 and DenseNet-121 pre-trained networks that are indulged in an image dataset representing stress and rest mode heart states acquired by SPECT. In experimentally evaluating the method, we explore a wide repertoire of deep learning network setups in conjunction with various robust evaluation and exploitation metrics. Additionally, to overcome the image dataset cardinality restrictions, we take advantage of the data augmentation technique expanding the set into an adequate number. Further evaluation of the model was performed via 10-fold cross-validation to ensure our model’s reliability. (3) Results: The proposed RGB-CNN model achieved an accuracy of 91.86%, while VGG-16 and DenseNet-121 reached 88.54% and 86.11%, respectively. (4) Conclusions: The abovementioned experiments verify that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.

show abstract

Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Παπανδριανός

Feleki

Papageorgiou

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…These radiological images are used in deep learning for tumor detection and classification; differentiation of benign, intermediate, and malignant tumors; segmentation of the region of tumors; and tumor grading prediction. In addition, bone scintigraphy, PET, and spectral CT are also good tools to detect bone metastasis (81)(82)(83)(84)(85)(86)(87)(88)(89)(90)(91)(92)(93), as evidenced by their ability to identify the primary lesion of the tumor and calculate the volume of the metastatic sites, and applying deep learning to these radiological images can improve our diagnosis of bone metastasis. With regard to the pathological images, it is demonstrated that these features from resected tissues can reveal tumor histopathology after hematoxylin and eosin (H&E) staining and can also be used for tumor classification, prognosis prediction, and treatment guidance.…”

Section: Deep Learning Applications In Medical Images For Bone Tumorsmentioning

confidence: 99%

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

et al. 2022

View full text Add to dashboard Cite

Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

show abstract

“…Automatic identification of diseases is a great contribution in the field of medicine. Therefore, the author in [ 10 ] performed classification using CNN to classify bone scintigraphy images. They also compared the well-known CNN architectures for image classification, including GoogleNet, VGG16, and the ResNet50.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive momentum method for medical image classification using convolutional neural network

2022

View full text Add to dashboard Cite

Background AI for medical diagnosis has made a tremendous impact by applying convolutional neural networks (CNNs) to medical image classification and momentum plays an essential role in stochastic gradient optimization algorithms for accelerating or improving training convolutional neural networks. In traditional optimizers in CNNs, the momentum is usually weighted by a constant. However, tuning hyperparameters for momentum can be computationally complex. In this paper, we propose a novel adaptive momentum for fast and stable convergence. Method Applying adaptive momentum rate proposes increasing or decreasing based on every epoch's error changes, and it eliminates the need for momentum hyperparameter optimization. We tested the proposed method with 3 different datasets: REMBRANDT Brain Cancer, NIH Chest X-ray, COVID-19 CT scan. We compared the performance of a novel adaptive momentum optimizer with Stochastic gradient descent (SGD) and other adaptive optimizers such as Adam and RMSprop. Results Proposed method improves SGD performance by reducing classification error from 6.12 to 5.44%, and it achieved the lowest error and highest accuracy compared with other optimizers. To strengthen the outcomes of this study, we investigated the performance comparison for the state-of-the-art CNN architectures with adaptive momentum. The results shows that the proposed method achieved the highest with 95% compared to state-of-the-art CNN architectures while using the same dataset. The proposed method improves convergence performance by reducing classification error and achieves high accuracy compared with other optimizers.

show abstract

Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy

Cited by 25 publications

References 21 publications

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

A novel adaptive momentum method for medical image classification using convolutional neural network

Contact Info

Product

Resources

About