4 research outputs found
Simultaneous cell detection and classification in bone marrow histology images
Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many computer-assisted diagnosis systems. Traditionally, cell detection and classification is performed as a sequence of two consecutive steps by using two separate deep learning networks, one for detection and the other for classification. This strategy inevitably increases the computational complexity of the training stage. In this paper, we propose a synchronized deep autoencoder network for simultaneous detection and classification of cells in bone marrow histology images. The proposed network uses a single architecture to detect the positions of cells and classify the detected cells, in parallel. It uses a curve-support Gaussian model to compute probability maps that allow detecting irregularly-shape cells precisely. Moreover, the network includes a novel neighborhood selection mechanism to boost the classification accuracy. We show that the performance of the proposed network is superior than traditional deep learning detection methods and very competitive compared to traditional deep learning classification networks. Runtime comparison also shows that our network requires less time to be trained
Towards Deep Cellular Phenotyping in Placental Histology
The placenta is a complex organ, playing multiple roles during fetal
development. Very little is known about the association between placental
morphological abnormalities and fetal physiology. In this work, we present an
open sourced, computationally tractable deep learning pipeline to analyse
placenta histology at the level of the cell. By utilising two deep
Convolutional Neural Network architectures and transfer learning, we can
robustly localise and classify placental cells within five classes with an
accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic
knowledge that is capable of both stratifying five distinct cell populations
and learn intraclass phenotypic variance. We envisage that the automation of
this pipeline to population scale studies of placenta histology has the
potential to improve our understanding of basic cellular placental biology and
its variations, particularly its role in predicting adverse birth outcomes.Comment: Updated MRC funding material. Corrected typo that suggested
ensembling and Inception accuracy were the same (updated to reflect the fact
the ensemble model is 1% better than previously reported