![]() This important contextual information, most of which is not present in the whole slide image, serves as our ground-truth label for the challenge. The goal of this challenge is to predict the stage of a patient’s cancer, using only the slide images generated by a breast biopsy.Ĭancer staging is a complex, multidisciplinary task: while it does take into account some features of the biopsy, it also integrates a wide variety of external information: the size of the lesion biopsied, its appearance and location on imaging, and a variety of other tests (imaging and more) to determine whether the cancer has spread to other locations in the body. ![]() Joseph, Nightingale OS, and The Association for Health Learning and Inference (AHLI)= developed this challenge in order to catalyze the development of algorithms that find new signal in digital pathology images, ultimately providing new insights into which patients may be at risk and need preventive treatment. Please refer to the full version of the dataset documentation as you get started to learn more about the cohort and key variables for this challenge including mortality and cancer stage. This dataset contains images and outcomes for 72,400 biopsy slides that correspond to 4,200 cases ranging from 2014 to 2020. But to date, the datasets linking biopsy images to patient outcomes-metastasis, death-have been far smaller than what is needed to apply modern approaches. Fascinatingly, these algorithms also hone in on features that humans neglect, for example, the nature of the non-cancerous tissue surrounding the tumor. There is already evidence that algorithms can predict which cancers will metastasize and harm patients on the basis of the biopsy image. These features, first identified in 1928, still underlie critical decisions today: which women must receive urgent treatment with surgery and chemotherapy? And which can be prescribed “watchful waiting”, sparing them invasive procedures for cancers that would not harm them? When a pathologist looks at a biopsy slide, she is looking for known signs of cancer: tubules, cells with atypical looking nuclei, evidence of rapid cell division. But death rates from metastatic breast cancer have hardly changed. Since the 1990s, we have found far more ‘cancers’, which has in turn prompted vastly more surgical procedures and chemotherapy. Underneath these routine tests lies a deep-and disturbing-mystery. Prize money and free compute credits have been announcedĮvery year, 40 million women get a mammogram some go on to have an invasive biopsy to better examine a concerning area. Predicting High Risk Breast Cancer: a Nightingale OS & AHLI data challengeĪTTENTION: Phase 1 is open until (ML4H submissions due Nov 14). ![]() Predicting High Risk Breast Cancer 2022 (3jmp2y128nxd).
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