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Meet Sri Lankan Researcher -- Jayakody Kankanamalage Chamani Shiranthika

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What are you currently working on or worked on before? I worked on international research projects related to Artificial Intelligence research areas. My main research area is reinforcement learning. Apart from that, I engaged in machine learning-related research projects related to personalized recommendations, cancer chemotherapy treatments, frailty analysis, cancer patients' survival rates analysis, etc. Other core research areas I have worked in areas like the travel industry, Internet, Internet of Things, air pollution, behavioral sciences computing, convolutional neural nets, environmental factors, health care,human-computer interaction, recommender systems, recurrent neural nets, sentiment analysis, social networking (online), time series, unsupervised learning, etc. I am seeking research collaboration opportunities, academic positions, industrial AI events, worldwide, and would love to work on collaborative projects.



Hospital uses AI to treat cervical cancer patient in UK first

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The Royal Surrey Foundation Trust treated Emma McCormick, 44, using adaptive radiotherapy after she was diagnosed with the cancer last April and was referred to St Luke's Cancer Centre. The treatment, called Ethos, involves a machine, created by healthcare company Varian, which uses artificial intelligence to deliver a prescription dose to tumours. The AI technology uses daily CT scans to target the specific areas that need radiotherapy, which helps avoid damage to healthy tissue and limit side-effects. Patients are required only to lay still on a flat surface inside the machine for the duration of the treatment. There is a screen above the machine which shows different images, and medical staff can play music to make the treatment more comfortable.


Personalized cancer screening with AI

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While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist: advocates argue for the ability to save lives, (women aged 60 to 69 had a 33 percent lower risk of dying compared to those who didn't get mammograms), and another camp argues about costly and potentially traumatic false positives (a meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography). Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch all: women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning and Health to ask: Can we use machine learning to provide personalized screening? Out of this came Tempo, a technology for creating risk-based screening guidelines.


How AI And Machine Learning Can Help Predict SDOH Needs - AI Summary

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Healthcare innovators are building proactive care management programs to mitigate SDOH risk by connecting high-risk members with community-based organizations to arrange food delivery, transportation to appointments, emergency housing and other services. In short, new organization- and provider-level emphasis on including SDOH along with traditional clinical diagnosis and utilization data is helping to "round out" the picture of patient populations targeted for care-management interventions. It is as if the expressed social need is now becoming recognized as the real barrier to realizing health goals – for example, completing a preventive service like breast, colorectal, prostate, cervical cancer screening or successfully controlling a chronic disease condition through medication adherence. Now, we see these social interventions occurring as a matter of daily work for accountable care organizations and physician networks participating in value-based payment arrangements with both commercial and government payers because quality, cost and patient satisfaction measures are key elements of their contract and connected to financial rewards. As an example, Genesis Physicians Group conducts individual interviews or surveys around SDOH and social needs that are highly connected to the risk of future adverse events that aren't easily incorporated into off-the-shelf predictive models.


Analysis of a Synthetic Breast Cancer Dataset in R

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This post is about me analyzing a synthetic dataset containing 60k records of patients with breast cancer.


How is machine learning advancing cancer research?

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Every day, each of the trillions of cells in the human body receives over 10,000 DNA lesions, which if unrepaired can lead to mutations and diseases, such as cancer. Luckily, we have complex machinery that detects and repairs DNA lesions; however, there is a lack of tools to study this machinery. Now, researchers from the Spanish National Cancer Research Center (CNIO; Madrid, Spain) and Massachusetts General Hospital (MGH; MA, USA) have used machine learning and high-throughput microscopy to visualize DNA repair machinery and identify new repair proteins, which could aid the development of novel cancer therapies.


Detect mitotic figures in whole slide images with Amazon Rekognition

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Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. Furthermore, histological assessment is a time-consuming process that requires highly trained professionals. With significant technological advances in the last decade, techniques such as whole slide imaging (WSI) and deep learning (DL) are now widely available.


Machine Learning

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From better healthcare access to improved food security, machine learning could tackle a wide range of challenges in developing countries. In 2020, a study published in Nature showed that Google's machine learning artificial intelligence programme, DeepMind AI, outperformed radiologists in detecting breast cancer. After being trained on thousands of mammograms, the system was able to accurately identify 89% of breast cancer cases, compared to radiologists' 74%. Just imagine what a difference the deployment of such a system could make in sub-Saharan Africa, where there are 0.2 doctors per 1000 people, according to the World Bank. Marilyn Moodley, Country Leader for South Africa and WECA (West, East, Central Africa) at SoftwareONE, says machine learning can help with some of the region's most pervasive problems, from reducing poverty and improving education to delivering healthcare and addressing sustainability challenges such as food demand.


Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification

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Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the more informative features; finally, we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC 2017 challeng...