937 research outputs found
Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image
Previous works on segmentation of SEM (scanning electron microscope) blood
cell image ignore the semantic segmentation approach of whole-slide blood cell
segmentation. In the proposed work, we address the problem of whole-slide blood
cell segmentation using the semantic segmentation approach. We design a novel
convolutional encoder-decoder framework along with VGG-16 as the pixel-level
feature extraction model. -e proposed framework comprises 3 main steps: First,
all the original images along with manually generated ground truth masks of
each blood cell type are passed through the preprocessing stage. In the
preprocessing stage, pixel-level labeling, RGB to grayscale conversion of
masked image and pixel fusing, and unity mask generation are performed. After
that, VGG16 is loaded into the system, which acts as a pretrained pixel-level
feature extraction model. In the third step, the training process is initiated
on the proposed model. We have evaluated our network performance on three
evaluation metrics. We obtained outstanding results with respect to classwise,
as well as global and mean accuracies. Our system achieved classwise accuracies
of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively,
while global and mean accuracies remain 97.18% and 91.96%, respectively.Comment: 13 pages, 13 figure
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
Deep Learning for Semantic Segmentation versus Classification in Computational Pathology: Application to mitosis analysis in Breast Cancer grading
Existing computational pathology approaches did not allow, yet, the emergence of effective/efficient computer-aided tools used as a second opinion for pathologists in the daily practice. Focusing on the case of computer-based qualification for breast cancer diagnosis, the present article proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consisted of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing with an F1-score of 94.35%, which is higher than the results from the literature using classical image processing techniques and also higher than the approaches using handcrafted features combined with CNNs. The second approach was an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6 which is higher than the results from the literature using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results showed the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last two chapters; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the described technology.Trabajo de investigació
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