scholarly journals Transfer learning from simulations improves the classification of OCT images of glandular epithelia

2020 ◽  
Author(s):  
Sassan Ostvar ◽  
Han Truong ◽  
Elisabeth R. Silver ◽  
Charles J. Lightdale ◽  
Chin Hur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Preventive Intervention ◽  
Synthetic Data ◽  
Standard Of Care ◽  
The United States ◽  
Tissue Imaging ◽  
Current Standard ◽  
Cad Systems

AbstractEsophageal adenocarcinoma (EAC) is a rare but lethal cancer with rising incidence in several global hotspots including the United States. The five-year survival rate for patients diagnosed with advanced disease can be as low as 5% in EAC, making early detection and preventive intervention crucial. The current standard of care for EAC targets patients with Barrett’s esophagus (BE), the main precursor to EAC and a relatively common condition in adults with chronic acid reflux disease. Preventive care for EAC requires repeated surveillance endoscopies of BE patients with biopsy sampling, and can be intrusive, error-prone, and costly. The integration of minimally-invasive subsurface tissue imaging in the current standard of care can reduce the need for exhaustive tissue sampling and improve the quality of life in BE patients. Effective adoption of subsurface imaging in EAC care can be facilitated by computer-aided detection (CAD) systems based on deep learning. Despite their recent successes in lung and breast cancer imaging, the development of deep neural networks for rare conditions like EAC remains challenging due to data scarcity, heavy bias in existing datasets toward non-cases, and uncertainty in image labels. Here we explore the use of synthetic datasets–specifically data derived from simulations of optical back-scattering during imaging– in the development of CAD systems based on deep learning. As a proof of concept, we studied the binary classification of esophageal OCT into normal squamous and glandular mucosae, typical of BE. We found that deep convolutional networks trained on synthetic data had improved performance over models trained on clinical datasets with uncertain labels. Model performance also improved with dataset size during training on synthetic data. Our findings demonstrate the utility of transfer from simulations to real data in the context of medical imaging, especially in the severely data-poor regime and when significant uncertainty in labels are present, and motivate further development of transfer learning from simulations to aid the development of CAD for rare malignancies.

scholarly journals Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Krisna Nuresa Qodri ◽  
Indah Soesanti ◽  
Hanung Adi Nugroho
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Transfer Learning ◽  
Medical Personnel ◽  
The United States ◽  
Brain Images ◽  
Training And Research ◽  
The Brain

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

scholarly journals Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020 ◽  
Vol 10 (6) ◽  
pp. 2021 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Main Idea ◽  
Diabetic Patients ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
Enormous Number ◽  
Deep Learning Model

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

scholarly journals Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Laith Alzubaidi ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Jinglan Zhang ◽  
Ye Duan
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Transfer Learning ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Learning Models ◽  
Sickle Cells ◽  
Microscopy Images

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

2019 ◽  
Vol 125 ◽  
pp. 1-6 ◽  
Author(s):  
SanaUllah Khan ◽  
Naveed Islam ◽  
Zahoor Jan ◽  
Ikram Ud Din ◽  
Joel J. P. C Rodrigues
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning

A decision-analytic model of idelalisib in relapsed or refractory patients with follicular lymphoma in the United States.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 30-30 ◽  
Author(s):  
Nathaniel Smith ◽  
Alexander Xenakis ◽  
Rachel Beckerman ◽  
Jagpreet Chhatwal ◽  
Stephanie A. Gregory ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Follicular Lymphoma ◽  
Survival Data ◽  
Treatment Options ◽  
Standard Of Care ◽  
The United States ◽  
Sensitivity Analyses ◽  
Decision Analytic Model ◽  
Current Standard ◽  
Utilization Data

30 Background: There are currently few treatment options for relapsed/refractory (RR) indolent non-Hodgkin’s lymphoma (iNHL) patients. Idelalisib (IDELA) is a first-in class PI3Kδ inhibitor with substantial clinical efficacy in iNHL patients refractory to rituximab and an alkylating agent. A single-arm clinical trial (Study 101-09) showed RR iNHL patients treated with IDELA have a median of 11 and 20.3 months of progression-free and overall survival (PFS and OS), respectively. Efficacy was also demonstrated in patients with iNHL subtypes such as follicular lymphoma (FL). The objective of this study was to project the health outcomes of IDELA versus the current standard of care for US FL patients. Methods: A partitioned survival model simulated a cohort of RR FL patients over 10 year time horizon. Patients first received IDELA or an aggregate comparator of current RR iNHL chemotherapy regimens in a progression-free state before transitioning to a progressive-disease state where they received palliative care until death. Survival data was fit and extrapolated from Study 101-09 (IDELA) for FL patients. A real-world database claims analysis provided survival, disease- and treatment-related adverse event (AEs) profiles, and medical resource utilization data for RR iNHL patients for the comparator. All outcomes were discounted at 3%. Results: Claims data predicted a median of 6.16 and 13.04 months of PFS and OS, respectively, for the comparator. Our model suggests that IDELA treatment improved health outcomes over 10 years versus the comparator, increasing life-months (LMs) and progression-free life-months (PFLMs) by 9.94 and 4.63 mos, respectively. Over 1 year, IDELA reduced both AEs and hospitalisations in FL patients by 40.3% and 49.8%, respectively. Deterministic and probabilistic sensitivity analyses demonstrated the model results are robust across different methods of survival extrapolation. Conclusions: IDELA was projected to improve health outcomes in RR FL patients compared to current treatments, largely driven by improved PFS and OS; short-term reductions in AEs and hospitalisation were specifically related to a delayed disease progression.

scholarly journals Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1579
Author(s):  
Wansuk Choi ◽  
Seoyoon Heo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
High Performance ◽  
Model Fit ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Video Classification ◽  
Video Clips ◽  
Performance Computing

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

scholarly journals Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2259-2262
Author(s):  
Alfiya Md. Shaikh
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Medical Condition ◽  
Literature Survey ◽  
Fundus Images ◽  
Fundus Image ◽  
Automatic Diagnosis ◽  
Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

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