Pooya Mobadersany

TL;DR: A computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers, which surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors.

TL;DR: This study illustrates how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrates performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma.

TL;DR: An approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers is described and it is shown how suggested Annotations generated by a weak algorithm can improve the accuracy of annotations generated by non-experts and can yield useful data for training segmentation algorithms without laborious manual tracing.

TL;DR: A novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei and results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality.

TL;DR: GestaltNet as discussed by the authors uses human-like behaviors of attention and aggregation to estimate the gestational age (GA) of scanned placental slides and compared it to a baseline model lacking the attention and aggregated functions.