9 research outputs found
Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models
According to some medical imaging techniques, breast histopathology images
called Hematoxylin and Eosin are considered as the gold standard for cancer
diagnoses. Based on the idea of dividing the pathologic image (WSI) into
multiple patches, we used the window [512,512] sliding from left to right and
sliding from top to bottom, each sliding step overlapping by 50% to augmented
data on a dataset of 400 images which were gathered from the ICIAR 2018 Grand
Challenge. Then use the EffficientNet model to classify and identify the
histopathological images of breast cancer into 4 types: Normal, Benign,
Carcinoma, Invasive Carcinoma. The EffficientNet model is a recently developed
model that uniformly scales the width, depth, and resolution of the network
with a set of fixed scaling factors that are well suited for training images
with high resolution. And the results of this model give a rather competitive
classification efficiency, achieving 98% accuracy on the training set and 93%
on the evaluation set.Comment: International Conference on Industrial Networks and Intelligent
Systems (INISCOM-2022), Springer, Vol. 444, pp. 152-16
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Диагностика МРТ-изображений опухолей головного мозга с использованием гибридных сверточных нейронечетких сетей
The problem of classification of brain tumors on medical images is considered. For its solution hybrid CNN-ANFIS is developed in which convolutional neural network VGG-16 and ResNetV2_50 are used as feature extractors while ANFIS is used as the classifier. Training algorithms of ANFIS were implemented. The experimental investigations of the suggested hybrid network on the standard dataset Brain MRI images for brain tumor detection were carried out and comparison with known results was performed
3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images
Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%
Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions
Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems.Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs.Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated.Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs