Paing, May

Preferred name

Paing, May

Alternative Name

Paing, May Phu

Paing, May Phyu

Main Affiliation

School of Engineering

may.pa@kmitl.ac.th

Now showing 1 - 10 of 32

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A computer aided diagnosis system for detection of lung nodules from series of CT slices
(2017-11-03)
Paing, May Phu
;
Choomchuay, Somsak
The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron (MLP) is used for classification and the accuracy 95% has been achieved.
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Ground glass opacity (GGO) nodules detection from lung CT scans
(2017-07-01)
Paing, May Phu
;
Choomchuay, Somsak
Ground glass opacity (GGO) nodules have a higher possibility of malignancy compared to other types of nodules appeared in the lung cancer. They are very effortful to detect due to their hazy structures and unclear margins. 65% of lung cancers are missed by the radiologists with the faint appearance of the GGO. Consequently, the detection of GGO is a critical issue and a striving task for the radiologists. This research proposes an automatic detection of GGO remained after the detection of solid opacity. Simple thresholding based on grey levels and mathematical image subtraction are applied for segmentation. Possible false after segmentation are reduced by the support vector machine (SVM). In total 37 GGOs, only 2 are missed by proposed segmentation and the false reduction performance is 94%.
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Automated segmentation of infarct lesions in t1‐weighted mri scans using variational mode decomposition and deep learning
(2021-03-02)
Paing, May Phu
;
Tungjitkusolmun, Supan
;
Bui, Toan Huy
;
Visitsattapongse, Sarinporn
;
Pintavirooj, Chuchart
Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1‐weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non‐infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep‐learning‐based segmentation task. Finally, a three‐dimensional U‐Net model is developed to perform patch‐wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset are utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.
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Improved Random Forest (RF) classifier for imbalanced classification of lung nodules
(2018-08-13)
Paing, May Phu
;
Choomchuay, Somsak
Computer-aided detection (CAD) for lung cancer acts a dynamic research in biomedical engineering. These CADs generally occur imbalanced data classification because there are a large number of false lesions which are non-nodules (majority class), compared to the actual nodules (minority class). This paper proposes an improved random forest (RF) classifier to solve the learning bias problem of the imbalanced classification. The proposed RF applies sampling and three feature selection schemes namely Relief, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to improve the classification performance.
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Comparing AI Decision-Making with Expert Biomarkers: A Case Study on Diabetic Retinopathy Classification
(2025-01-01)
Sharma, Aayushma
;
Paing, May Phu
Artificial intelligence (AI) has become prevalent in the healthcare sector due to its ability to interpret complex medical images that may not be apparent to humans. Traditional black-box models were used to classify the disease, providing no information as to why certain things were labeled as such and not others. This paper utilizes the use of eXplainable-AI (XAI), specifically, Layer-wise Relevance Propagation (LRP) which generates mapping between AI decision and biomarker used by the ophthalmologist whereby enhancing results interpretability and transparency in the disease diagnostic tasks. VGG-16 incorporated with batch normalization and label smoothing was used for the classification tasks whereas LRP was employed to perform the heat-map generation to see if the feature extracted and used by AI was consistent with the experts' biomarkers. Our proposed model obtained a classification accuracy of 77.33%, where 165 out of 266 images were aligned with the ophthalmologist's prediction. Furthermore, the significance of heatmap generation was supported by a one-sample Z-test which revealed that the alignment between AI predictions and expert biomarkers is significantly greater than random, with a 95% confidence interval.
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Automatic detection of mediastinal lymph nodes using 3D convolutional neural network
(2019-09-16)
Paing, May Phu
;
Pintavirooj, Chuchart
;
Tungjitkusolmun, Supan
;
Win, Kyi Pyar
;
Hamamoto, Kazuhiko
Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of lung cancer. As the lymph nodes are low in attenuation and cluttering with various shapes and sizes, manual detection is usually error-prone and effort-intensive. This paper introduces a method for automatic detection of mediastinal lymph nodes by proposing three significant contributions. First, we constraint the detection area, mediastinal region, using greylevel thresholding. Next, we apply the watershed method and hessian eigenvalues to separate a cluster of lymph nodes. Finally, we build a three-dimensional convolutional neural network (3D CNN) to distinguish the actual lymph nodes from other false lesions. Our experiment is conducted using 70 CT exams containing 314 lymph nodes and achieved a favorable result with 94 % detection rate.
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Instance segmentation of cells and nuclei from multi-organ cross-protocol microscopic images
(2024-09-01)
Baral, Sushish
;
Paing, May Phu
Background: Light microscopy is a widely used technique in cell biology due to its satisfactory resolution for cellular structure analysis, prevalent availability of fluorescent probes for staining, and compatibility for the dynamic analysis of live cells. However, the segmentation of cells and nuclei from microscopic images is not a straightforward process because it has several challenges such as high variation in morphology and shape, the presence of noise and diverse contrast in backgrounds, clustering or overlapping nature of cells. Dealing with these challenges and facilitating more reliable analysis necessitates the implementation of computer-aided methods that leverage image processing techniques and deep learning algorithms. The major goal of this study is to propose a model, for instance segmentation of cells and nuclei, applying the most cutting-edge deep learning techniques. Methods: A fine-tuned You Only Look at Once version 9 extended (YOLOv9-E) model is initially applied as a prompt generator to generate bounding box prompts. Using the generated prompts, a pre-trained segment anything model (SAM) is subsequently applied through zero-short inferencing to produce raw segmentation masks. These segmentation masks are then refined using non-max suppression and simple image processing methods such as image addition and morphological processing. The proposed method is developed and evaluated using an open-sourced dataset called Expert Visual Cell Annotation (EVICAN), which is relatively large and contains 4,738 microscopy images extracted from cross organs using different protocols. Results: Based on the evaluation results on three different levels of EVICAN test sets, the proposed method demonstrates noticeable performances showing average mAP50 [mean average precision at intersection over union (IoU) =0.50] scores of 96.25, 95.05, and 94.18 for cell segmentation, and 68.04, 54.66, and 38.29 for nucleus segmentation on easy, medium, and difficult test sets, respectively. Conclusions: Our proposed method for instance segmentation of cells and nuclei provided favorable performance compared to the existing methods in literature, indicating its potential utility as an assistive tool for cell culture experts, facilitating prompt and reliable analysis.
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Explainable AI (XAI) for Breast Cancer Diagnosis
(2024-01-01)
Ariyametkul, Awika
;
Tamang, Sudarshan
;
Paing, May Phu
Breast cancer is the leading cause of mortality and incidence among women worldwide. Mammography, an essential imaging technique, plays a pivotal role in both screening and diagnostic processes by facilitating early detection, which helps improve survival rates. Despite its effectiveness, interpreting mammographic images presents considerable challenges, necessitating the expertise of highly trained radiologists. Artificial intelligence (AI) is a powerful tool for managing large amounts of data and is increasingly used across numerous sectors, including medical applications. This research focuses on applying Convolutional Neural Networks (CNNs) to classify breast cancer from mammograms. We explored six different CNN models including simple ConvNet, AlexNet, VGG-16, GoogLeNet, XceptionNet, and DenseNet201. Our results indicate that DenseNet201 is the most suitable model for this task, achieving 99% accuracy. However, a limitation of AI is the lack of transparency and explanation, often referred to as the 'black box' problem. This vulnerability can be addressed through explainable artificial intelligence (XAI), which elucidates the processes behind AI's decision-making. We employed three different XAI methodologies, including LIME, GradCAM, and GradCAM++, to visualize the model's decision-making process.
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Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN
(2022-01-01)
Paing, May Phu
;
Sento, Adna
;
Bui, Toan Huy
;
Pintavirooj, Chuchart
Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.
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Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
(2022-12-01)
Tamang, Thinam
;
Baral, Sushish
;
Paing, May Phu
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.

Paing, May

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