Crowd disagreement about medical images is informative

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at \url{https://figshare.com/s/5cbbce14647b66286544}.Comment: Accepted for publication at MICCAI LABELS 201

Artificially induced changes of butterfly wing colour patterns: dynamic signal interactions in eyespot development

Eyespot formation in butterfly wings has been explained by the concentration gradient model. However, this model has recently been questioned, and dynamic interactions between the black-inducing signal and its inhibitory signal have been proposed. Here, the validity of these models was examined using a nymphalid butterfly Junonia almana. Early focal damage to the major eyespots often made them smaller, whereas the late damage made the outer ring larger and the inner ring smaller in a single eyespot. Non-focal damage at the outer ring not only attracted the whole eyespot structure toward the damaged site but also reduced the overall size of the eyespot. Surprisingly, a reduction of the major eyespot was accompanied by an enlargement of the associated miniature eyespots. These results demonstrate limitations of the conventional gradient model and support a dynamic interactive nature of morphogenic signals for colour-pattern determination in butterfly wings

Widespread aberrant alternative splicing despite molecular remission in chronic myeloid leukaemia patients

Vast transcriptomics and epigenomics changes are characteristic of human cancers, including leukaemia. At remission, we assume that these changes normalise so that omics-profiles resemble those of healthy individuals. However, an in-depth transcriptomic and epigenomic analysis of cancer remission has not been undertaken. A striking exemplar of targeted remission induction occurs in chronic myeloid leukaemia (CML) following tyrosine kinase inhibitor (TKI) therapy. Using RNA sequencing and whole-genome bisulfite sequencing, we profiled samples from chronic-phase CML patients at diagnosis and remission and compared these to healthy donors. Remarkably, our analyses revealed that abnormal splicing distinguishes remission samples from normal controls. This phenomenon is independent of the TKI drug used and in striking contrast to the normalisation of gene expression and DNA methylation patterns. Most remarkable are the high intron retention (IR) levels that even exceed those observed in the diagnosis samples. Increased IR affects cell cycle regulators at diagnosis and splicing regulators at remission. We show that aberrant splicing in CML is associated with reduced expression of specific splicing factors, histone modifications and reduced DNA methylation. Our results provide novel insights into the changing transcriptomic and epigenomic landscapes of CML patients during remission. The conceptually unanticipated observation of widespread aberrant alternative splicing after remission induction warrants further exploration. These results have broad implications for studying CML relapse and treating minimal residual disease.Ulf Schmitz, Jaynish S. Shah, Bijay P. Dhungel, Geoffray Monteuuis, Phuc-Loi Luu, Veronika Petrova, Cynthia Metierre, Shalima S. Nair, Charles G. Bailey, Verity A. Saunders, Ali G. Turhan, Deborah L. White, Susan Branford, Susan J. Clark, Timothy P. Hughes, Justin J.-L. Wong, and John E.J. Rask

Crowd disagreement about medical images is informative

Classifiers for medical image analysis are often trained with a single consensus label, based on combining labels given by experts or crowds. However, disagreement between annotators may be informative, and thus removing it may not be the best strategy. As a proof of concept, we predict whether a skin lesion from the ISIC 2017 dataset is a melanoma or not, based on crowd annotations of visual characteristics of that lesion. We compare using the mean annotations, illustrating consensus, to standard deviations and other distribution moments, illustrating disagreement. We show that the mean annotations perform best, but that the disagreement measures are still informative. We also make the crowd annotations used in this paper available at https://figshare.com/s/5cbbce14647b66286544