48 research outputs found
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performance in
object recognition tasks and dense classification problems such as semantic
segmentation. However, training deep neural networks on large and sparse
datasets is still challenging and can require large amounts of computation and
memory. In this work, we address the task of performing semantic segmentation
on large data sets, such as three-dimensional medical images. We propose an
adaptive sampling scheme that uses a-posterior error maps, generated throughout
training, to focus sampling on difficult regions, resulting in improved
learning. Our contribution is threefold: 1) We give a detailed description of
the proposed sampling algorithm to speed up and improve learning performance on
large images. We propose a deep dual path CNN that captures information at fine
and coarse scales, resulting in a network with a large field of view and high
resolution outputs. We show that our method is able to attain new
state-of-the-art results on the VISCERAL Anatomy benchmark
Deep Learning in Medical Imaging
Medical image processing tools play an important role in clinical routine in helping doctors to establish whether a patient has or does not have a certain disease. To validate the diagnosis results, various clinical parameters must be defined. In this context, several algorithms and mathematical tools have been developed in the last two decades to extract accurate information from medical images or signals. Traditionally, the extraction of features using image processing from medical data are time-consuming which requires human interaction and expert validation. The segmentation of medical images, the classification of medical images, and the significance of deep learning-based algorithms in disease detection are all topics covered in this chapter
Classification of Breast Cancer Histopathological Images Using Semi-Supervised GANs
Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new CNN. Deep neural networks are trained in a generative adversarial fashion in a semi-supervised environment by extracting low-level features that improve classification accuracy. This paper proposes an eloquent approach to classifying histopathological images accurately using Semi-Supervised GANs with a classification accuracy greater than 93%
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
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure