274 research outputs found
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Behavioral changes are the earliest signs of a mental disorder, but arguably,
the dynamics of brain function gets affected even earlier. Subsequently,
spatio-temporal structure of disorder-specific dynamics is crucial for early
diagnosis and understanding the disorder mechanism. A common way of learning
discriminatory features relies on training a classifier and evaluating feature
importance. Classical classifiers, based on handcrafted features are quite
powerful, but suffer the curse of dimensionality when applied to large input
dimensions of spatio-temporal data. Deep learning algorithms could handle the
problem and a model introspection could highlight discriminatory
spatio-temporal regions but need way more samples to train. In this paper we
present a novel self supervised training schema which reinforces whole sequence
mutual information local to context (whole MILC). We pre-train the whole MILC
model on unlabeled and unrelated healthy control data. We test our model on
three different disorders (i) Schizophrenia (ii) Autism and (iii) Alzheimers
and four different studies. Our algorithm outperforms existing self-supervised
pre-training methods and provides competitive classification results to
classical machine learning algorithms. Importantly, whole MILC enables
attribution of subject diagnosis to specific spatio-temporal regions in the
fMRI signal.Comment: Accepted at MICCAI 2020. arXiv admin note: substantial text overlap
with arXiv:1912.0313
Chimpanzee face recognition from videos in the wild using deep learning
Video recording is now ubiquitous in the study of animal behavior, but its analysis on a large scale is prohibited by the time and resources needed to manually process large volumes of data. We present a deep convolutional neural network (CNN) approach that provides a fully automated pipeline for face detection, tracking, and recognition of wild chimpanzees from long-term video records. In a 14-year dataset yielding 10 million face images from 23 individuals over 50 hours of footage, we obtained an overall accuracy of 92.5% for identity recognition and 96.2% for sex recognition. Using the identified faces, we generated co-occurrence matrices to trace changes in the social network structure of an aging population. The tools we developed enable easy processing and annotation of video datasets, including those from other species. Such automated analysis unveils the future potential of large-scale longitudinal video archives to address fundamental questions in behavior and conservation.Agência financiadora Número do subsídio
Engineering & Physical Sciences Research Council (EPSRC)
EP/M013774/1
Cooperative Research Program of Primate Research Institute, Kyoto University
Google
Clarendon Fund
Boise Trust Fund
Wolfson College, University of Oxford
Leverhulme Trust
PLP-2016-114
Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)
Japan Society for the Promotion of Science
16H06283
Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)
Japan Society for the Promotion of Science
LGP-U04info:eu-repo/semantics/publishedVersio
Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.
Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data
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