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Integrating medical imaging and cancer biology with deep neural networks


Credit: Smedley, Aberle, and Hsu, doi 10.1117/1.JMI.8.3.031906.
Despite our remarkable advances in medicine and healthcare, the cure to cancer continues to elude us. On the bright side, we have made considerable progress in detecting several cancers in earlier stages, allowing doctors to provide treatments that increase long-term survival. The credit for this is due to integrated diagnosis, an approach to patient care that combines molecular information and medical imaging data to diagnose the cancer type and, eventually, predict treatment outcomes.
There are, however, several intricacies involved. The correlation of molecular patterns, such as gene expression and mutation, with image features (e.g., how a tumor appears in a CT scan), is commonly referred to as radiogenomics. This field is limited by its frequent use of high-dimensional data, wherein the number of features exceeds that of observations. Radiogenomics is also plagued by several simplifying model assumpti ....

United States , Despina Kontos , William Hsu , Los Angeles , Karen Drukker , Denise Aberle , Nova Smedley , University Of California , University Of Chicago , University Of Pennsylvania , Integrated Diagnostics Shared , ஒன்றுபட்டது மாநிலங்களில் , வில்லியம் ஶூ , லாஸ் ஏஞ்சல்ஸ் , மறுக்க அபேர்லே , பல்கலைக்கழகம் ஆஃப் கலிஃபோர்னியா , பல்கலைக்கழகம் ஆஃப் சிகாகோ , பல்கலைக்கழகம் ஆஃப் பென்சில்வேனியா ,