A5088616124- AI in cancer detection
- Multilevel Inverters and Converters
- Fault Detection and Control Systems
- Image Retrieval and Classification Techniques
- Machine Fault Diagnosis Techniques
- Cancer Genomics and Diagnostics
- Radiomics and Machine Learning in Medical Imaging
- Silicon Carbide Semiconductor Technologies
- Linguistics and Cultural Studies
- Cultural and Sociopolitical Studies
- Genital Health and Disease
- Prostate Cancer Diagnosis and Treatment
- Colorectal Cancer Screening and Detection
- Turkish Literature and Culture
- Molecular Biology Techniques and Applications
- Perfectionism, Procrastination, Anxiety Studies
- Data Stream Mining Techniques
- Breast Cancer Treatment Studies
- Advanced Image and Video Retrieval Techniques
- Text and Document Classification Technologies
- Video Surveillance and Tracking Methods
- Advanced Clustering Algorithms Research
- Electromagnetic Compatibility and Noise Suppression
- Advanced DC-DC Converters
- Advanced Text Analysis Techniques
Bahçeşehir University
2022-2025
Agency for Science, Technology and Research
2020-2023
Bioinformatics Institute
2020-2023
National University of Singapore
2019-2023
Middle East Technical University
2017-2023
University of Edinburgh
2022-2023
Yüksek İhtisas Üniversitesi
2014
<h3>Importance</h3> Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. <h3>Objective</h3> Assess the performance automated at detecting metastases in hematoxylin eosin–stained tissue sections lymph nodes women with breast cancer compare it pathologists' diagnoses a setting. <h3>Design, Setting, Participants</h3> Researcher challenge competition (CAMELYON16) develop solutions for node (November 2015-November...
In the era of advancing digital pathology, integrating slides and deep learning-based decision support systems has become increasingly prevalent in clinical practice. The effectiveness these heavily relies on tissue region segmentation, a process essential for slide scanning success learning (DL) models. While thresholding-based methods are fast, they usually do not accurately detect regions, especially diverse staining scenarios or debris-laden images. Hence, this study develops...
Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime electrical machine. In induction machine (IM) driven by inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced due to fault-controller interaction. Based on this observation, study developed neural network models using switching statistics detect ISCF IM. The method was non-invasive, it did not require any additional sensors. task, area under receiver...
Histopathology is a crucial diagnostic tool in cancer and involves the analysis of gigapixel slides. Multiple instance learning (MIL) promises success digital histopathology thanks to its ability handle slides work with weak labels. MIL machine paradigm that learns mapping between bags instances bag It represents slide as patches uses slide's label bag's label. This paper introduces distribution-based pooling filters obtain bag-level representation by estimating marginal distributions...
Interleaving can be employed to reduce ripples in multiphase DC-DC converters although phases are operated under asymmetric conditions such as different input voltages or loads. To allow ripple minimization conditions, phase shifts between the switch timings of have appropriately adjusted. This study presents a method based on artificial neural networks (ANN) that provide required minimize conditions. obtain machine learning dataset, set optimal phaseshift angles minimizing common output...
This article discusses the effect of segregation histopathology images data into three sets; training set for machine learning model, validation model selection and test testing performance. We found that one must be cautious when segregating histological (slides) training, sets because subtle mishandling can introduce leakage gives illusively good results on set. performed this study gene mutation prediction performance by using deep neural network in paper Coudray et al. [1]. By provided...
There are different multiple instance learning (MIL) pooling filters used in MIL models. In this paper, we study the effect of on performance models real world tasks. We designed a neural network based framework with 5 filters: `max', `mean', `attention', `distribution' and `distribution attention'. also formulated tasks lymph node metastases dataset. found that our task is for filters. observed performances five from to task. Hence, selection correct filter each crucial better performance....
Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within core. They may miss glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed multi-resolution deep-learning pipeline to assist pathologists detecting cases. Analyzing gland at multiple resolutions, our model exploited morphology neighborhood information, which were crucial classification. We tested on...
A weakly supervised learning based clustering framework is proposed in this paper. As the core of framework, we introduce a novel multiple instance task on bag level label called unique class count ($ucc$), which number classes among all instances inside bag. In task, no annotations individual are needed during training models. We mathematically prove that with perfect $ucc$ classifier, bags possible even when given training. have constructed neural network classifier and experimentally...
Bu araştırmanın amacı, sosyal kaygı ile başa çıkma psiko eğitim programının ortaokul 6. ve 7. sınıf öğrencilerinin düzeylerini azaltmada etkili olup olmadığını incelemektir.
Abstract Pathologists diagnose prostate cancer by core needle biopsy. For low-grade and low-volume cases, the pathologists look for few malignant glands out of hundreds within a core. They may miss glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed multi-resolution deep learning pipeline detecting to help effectively accurately cases. The consisted two stages: gland segmentation model detected sections classified each into benign vs. malignant....
Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime electrical machine. In induction machine (IM) driven by inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced due to fault-controller interaction. Based on this observation, study developed neural network using switching statistics detect ISCF IM. The method was non-invasive, it did not require any additional sensors. task, achieved area under receiver...
<p>Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime electrical machine. In induction machine (IM) driven by inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced due to fault-controller interaction. Based on this observation, study developed neural network models using switching statistics detect ISCF IM. The method was non-invasive, it did not require any additional sensors. task, area under...
Abstract Tumor purity is the proportion of cancer cells in tumor tissue. An accurate estimation crucial for pathologic evaluation and sample selection to minimize normal cell contamination high throughput genomic analysis. We developed a novel deep multiple instance learning model predicting from H&E stained digital histopathology slides. Our successfully predicted slides fresh-frozen sections eight different TCGA cohorts formalin-fixed paraffin-embedded local Singapore cohort. The...
<p>Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime electrical machine. In induction machine (IM) driven by inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced due to fault-controller interaction. Based on this observation, study developed neural network models using switching statistics detect ISCF IM. The method was non-invasive, it did not require any additional sensors. task, area under...
Oner, an early-career researcher, and Lee Sung, group leaders, have developed a deep learning model for accurate prediction of the proportion cancer cells within tumor tissue. This is necessary step precision oncology target therapy in cancer. They talk about their view data science evolution pathology coming years.
<p>Early detection of an inter-turn short circuit fault (ISCF) can reduce repair costs and downtime electrical machine. In induction machine (IM) driven by inverter with a model predictive control (MPC) algorithm, the controller outputs are influenced due to fault-controller interaction. Based on this observation, study developed neural network models using switching statistics detect ISCF IM. The method was non-invasive, it did not require any additional sensors. task, area under...