Microsoft uses AI to diagnose cervical cancer faster in India – TechCrunch

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More women in India die from cervical cancer than in any other country. This preventable disease kills around 67,000 women in India every year, more than 25% of the 260,000 deaths worldwide. Effective screening and early detection can help reduce its incidence, but part of the challenge -- and there are several parts -- today is that the testing process to detect the onset of the disease is unbearably time-consuming. This is because the existing methodology that cytopathologists use is time consuming to begin with, but also because there are very few of them in the nation. Could AI speed this up?


Microsoft is using AI to screen for cervical cancer

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SRL Diagnostics (the largest diagnostics laboratory company in India), which has been working with Microsoft to develop digital pathology services, has begun assessing its Cervical Cancer Image Detection application programming interface (API). The Cervical Cancer Image Detection API operates on Microsoft's Azure, and the research has been conducted in Mumbai. According to TechCrunch the system is capable of rapidly screening liquid-based cytology slide images in order to detect cervical cancer at the early stages. Digital data is then transmitted back to pathologists in the originating laboratory. Detecting cervical cancer early is key to increasing survival rates.


Algorithms for screening of Cervical Cancer: A chronological review

arXiv.org Machine Learning

There are various algorithms and methodologies used for automated screening of cervical cancer by segmenting and classifying cervical cancer cells into different categories. This study presents a critical review of different research papers published that integrated AI methods in screening cervical cancer via different approaches analyzed in terms of typical metrics like dataset size, drawbacks, accuracy etc. An attempt has been made to furnish the reader with an insight of Machine Learning algorithms like SVM (Support Vector Machines), GLCM (Gray Level Co-occurrence Matrix), k-NN (k-Nearest Neighbours), MARS (Multivariate Adaptive Regression Splines), CNNs (Convolutional Neural Networks), spatial fuzzy clustering algorithms, PNNs (Probabilistic Neural Networks), Genetic Algorithm, RFT (Random Forest Trees), C5.0, CART (Classification and Regression Trees) and Hierarchical clustering algorithm for feature extraction, cell segmentation and classification. This paper also covers the publicly available datasets related to cervical cancer. It presents a holistic review on the computational methods that have evolved over the period of time, in chronological order in detection of malignant cells.


Microsoft AI helps diagnose cervical cancer faster

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In some cases, AI-assisted cancer detection might be more than a convenience -- it could be the key to getting a diagnosis in the first place. Microsoft and SRL Diagnostics have developed an AI tool that helps detect cervical cancer, freeing doctors in India and other countries where the sheer volume of patients could prove overwhelming. The team trained an AI to spot signs of the cancer by feeding it "thousands" of annotated cervical smear images to help it spot abnormalities (including pre-cancerous examples) that warrant a closer look. Doctors would only have to look at those slides that justify real concern. A framework for using the AI is now ready for an "internal preview" at SRL.


Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images

arXiv.org Artificial Intelligence

We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images. Inspired by how human experts perform diagnosis, our approach first identifies and classifies diagnostic image regions containing informative thyroid cells, which only comprise a tiny fraction of the entire image. These local estimates are then aggregated into a single prediction of thyroid malignancy. Several unique characteristics of thyroid cytopathology guide our deep-learning-based approach. While our method is closely related to multiple-instance learning, it deviates from these methods by using a supervised procedure to extract diagnostically relevant regions. Moreover, we propose to simultaneously predict thyroid malignancy, as well as a diagnostic score assigned by a human expert, which further allows us to devise an improved training strategy. Experimental results show that the proposed algorithm achieves performance comparable to human experts, and demonstrate the potential of using the algorithm for screening and as an assistive tool for the improved diagnosis of indeterminate cases.