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Tag: deep learning

Tuning Algorithms to the Histology Lab

The promise of an effective set of tools based on deep learning or other machine learning algorithms is the current buzz of the digital pathology markets. While the evolving tools, models and techniques are producing strongly positive results, there are still many factors which impact the utility and portability of models and tools being created across real-world data sets.

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Distinction of Benign and Malignant Lesions through Deep Learning

Machine learning is already prevalent in many industries and most pathologists are unaware how accessible machine learning is and how it can be used to augment their work or research. Applications include decision support, image analytics, process improvement, disease diagnosis and prognosis.

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Image Based Risk Assessment in Cancer

Pathologists identify and interpret the changes that characterise diseases in cells and tissues, both for the studying/understanding disease processes in general and obtaining clinically relevant information for individual patients. Historically, by examining biopsy specimens, pathologists identified whether a lesion was neoplastic, inflammatory, or some other broad category. As medicine evolved, the task evolved into identifying more specific classifications. For example, if it was not sufficient to make the diagnosis of cancer; it was necessary to identify the specific subtype and grade of cancer in order to inform treatment decisions that were becoming increasingly sophisticated.

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Deep Learning in Digital Pathology

“Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you provide, the better it is” – Geoffrey Hinton (Google)

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