48,233 research outputs found
Learning Deep Neural Networks for Enhanced Prostate Histological Image Analysis
In recent years, deep convolutional neural networks (CNNs) have shown
promise for improving prostate cancer diagnosis by enabling quantitative
histopathology through digital pathology. However, there are a number of
factors that limit the widespread adoption and clinical utility of deep learning
for digital pathology. One of these limitations is the requirement for large
labelled training datasets which are expensive to construct due to limited availability
of the requisite expertise. Additionally, digital pathology applications
typically require the digitisation of histological slides at high magnifications.
This process can be challenging especially when digitising large histological
slides such as prostatectomies.
This work studies and addresses these issues in two important applications
of digital pathology: prostate nuclei detection and cell type classification. We
study the performance of CNNs at different magnifications and demonstrate
that it is possible to perform nuclei detection in low magnification prostate
histopathology using CNNs with minimal loss in accuracy. We then study the
training of prostate nuclei detectors in the small data setting and demonstrate
that although it is possible to train nuclei detectors with minimal data, the
models will be sensitive to hyperparameter choice and therefore may not generalise
well. Instead, we show that pre-training the CNNs with colon histology
data makes them more robust to hyperparameter choice.
We then study the CNN performance for prostate cell type classification
using supervised, transfer and semi-supervised learning in the small data setting.
Our results show that transfer learning can be detrimental to performance but semi-supervised learning is able to provide significant improvements to the
learning curve, allowing the training of neural networks with modest amounts
of labelled data. We then propose a novel semi-supervised learning method
called Deeply-supervised Exemplar CNNs and demonstrate their ability to improve
the cell type classifier learning curves at a much better rate than previous
semi-supervised neural network methods
Infant Cry Signal Processing, Analysis, and Classification with Artificial Neural Networks
As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals.
We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures the diversity of variations within infant cries and the result outperforms all other related studies on asphyxiated baby crying classification. We propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of infant vocal tract development as early as 4-month age. Experiments discover the pattern and tendency of the vocal tract changes and predict the abnormality of infant vocal tract by classifying the cry signals into younger age category. We propose an approach of generating hybrid feature set and using prior knowledge in a multi-stage CNNs model for robust infant sound classification. The dominant and auxiliary features within the set are beneficial to enlarge the coverage as well as keeping a good resolution for modeling the diversity of variations within infant sound and the experimental results give encouraging improvements on two relative databases. We propose an approach of graph convolutional network (GCN) with transfer learning for robust infant cry reason classification. Non-fully connected graphs based on the similarities among the relevant nodes are built to consider the short-term and long-term effects of infant cry signals related to inner-class and inter-class messages. With as limited as 20% of labeled training data, our model outperforms that of the CNN model with 80% labeled training data in both supervised and semi-supervised settings. Lastly, we apply mel-spectrogram decomposition to infant cry classification and propose a fusion method to further improve the infant cry classification performance
Universal Language Model Fine-tuning for Text Classification
Inductive transfer learning has greatly impacted computer vision, but
existing approaches in NLP still require task-specific modifications and
training from scratch. We propose Universal Language Model Fine-tuning
(ULMFiT), an effective transfer learning method that can be applied to any task
in NLP, and introduce techniques that are key for fine-tuning a language model.
Our method significantly outperforms the state-of-the-art on six text
classification tasks, reducing the error by 18-24% on the majority of datasets.
Furthermore, with only 100 labeled examples, it matches the performance of
training from scratch on 100x more data. We open-source our pretrained models
and code.Comment: ACL 2018, fixed denominator in Equation 3, line
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