Simon Graham

TL;DR: A novel convolutional neural network is presented for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass to separate clustered nuclei, resulting in an accurate segmentation.

TL;DR: Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics as well as heavy data augmentation in the MoNuSeg 2018 challenge.

TL;DR: A fully convolutional neural network is proposed that counters the loss of information caused by max‐pooling by re‐introducing the original image at multiple points within the network, and introduces random transformations during test time for an enhanced segmentation result that simultaneously generates an uncertainty map, highlighting areas of ambiguity.

TL;DR: The achieved results are promising given the difficulty of the tasks and weakly‐labeled nature of the ground truth, however, further research is needed to improve the practical utility of image analysis methods for this task.

TL;DR: Two computer algorithms are presented; one designed for segmentation of nuclei and the other for classification of whole slide tissue images, both of which were evaluated in the MICCAI 2017 Digital Pathology challenge.