423,499 research outputs found
Deep Learning for Semantic Segmentation versus Classification in Computational Pathology: Application to mitosis analysis in Breast Cancer grading
Existing computational pathology approaches did not allow, yet, the emergence of effective/efficient computer-aided tools used as a second opinion for pathologists in the daily practice. Focusing on the case of computer-based qualification for breast cancer diagnosis, the present article proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consisted of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing with an F1-score of 94.35%, which is higher than the results from the literature using classical image processing techniques and also higher than the approaches using handcrafted features combined with CNNs. The second approach was an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6 which is higher than the results from the literature using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results showed the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last two chapters; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the described technology.Trabajo de investigació
Recommended from our members
Learning from Sequential User Data: Models and Sample-efficient Algorithms
Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited\u27 datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study how to introduce prior knowledge in the deep networks to maximize prediction performance. We focus on four sequential tasks: computerized adaptive testing in psychometrics, sketching in recommender systems, knowledge tracing in computer-assisted education, and career path modeling in the labor market.
In the first two tasks, we devise novel sample-efficient algorithms to query a minimal number of sequential samples to improve future predictions. We propose a Bilevel Optimization-Based framework for computerized adaptive testing to learn a data-driven question selection algorithm that improves existing data selection policies. We also tackle the sketching problem in the recommender system, with the task of recommending the next item using a stored subset of prior data samples. In this setting, we develop a data-driven sequential selection algorithm that tackles evolving downstream task distribution. In the last two tasks, we devise novel neural models to introduce prior knowledge exploiting limited data samples. For knowledge tracing, we propose a novel neural architecture, inspired by cognitive and psychometric models, to improve the prediction of students\u27 future performance and utilize the labeled data samples efficiently. For career path modeling, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals.
The data-driven differentiable data selection algorithms for the first two tasks open up future directions to query (a non-differentiable operation) a minimal number of samples optimally to maximize prediction performance. The structures, introduced in the neural architecture for the models in the last two tasks using prior knowledge, open up future directions to learn deep models augmented with prior knowledge using limited data samples
Instance-based Deep Transfer Learning
Deep transfer learning recently has acquired significant research interest.
It makes use of pre-trained models that are learned from a source domain, and
utilizes these models for the tasks in a target domain. Model-based deep
transfer learning is probably the most frequently used method. However, very
little research work has been devoted to enhancing deep transfer learning by
focusing on the influence of data. In this paper, we propose an instance-based
approach to improve deep transfer learning in a target domain. Specifically, we
choose a pre-trained model from a source domain and apply this model to
estimate the influence of training samples in a target domain. Then we optimize
the training data of the target domain by removing the training samples that
will lower the performance of the pre-trained model. We later either fine-tune
the pre-trained model with the optimized training data in the target domain, or
build a new model which is initialized partially based on the pre-trained
model, and fine-tune it with the optimized training data in the target domain.
Using this approach, transfer learning can help deep learning models to capture
more useful features. Extensive experiments demonstrate the effectiveness of
our approach on boosting the quality of deep learning models for some common
computer vision tasks, such as image classification.Comment: Accepted to WACV 2019. This is a preprint versio
Accelerating Deep Learning with Shrinkage and Recall
Deep Learning is a very powerful machine learning model. Deep Learning trains
a large number of parameters for multiple layers and is very slow when data is
in large scale and the architecture size is large. Inspired from the shrinking
technique used in accelerating computation of Support Vector Machines (SVM)
algorithm and screening technique used in LASSO, we propose a shrinking Deep
Learning with recall (sDLr) approach to speed up deep learning computation. We
experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network
(DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data
sets. Results show that the speedup using shrinking Deep Learning with recall
(sDLr) can reach more than 2.0 while still giving competitive classification
performance.Comment: The 22nd IEEE International Conference on Parallel and Distributed
Systems (ICPADS 2016
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Deep learning models learn to fit training data while they are highly
expected to generalize well to testing data. Most works aim at finding such
models by creatively designing architectures and fine-tuning parameters. To
adapt to particular tasks, hand-crafted information such as image prior has
also been incorporated into end-to-end learning. However, very little progress
has been made on investigating how an individual training sample will influence
the generalization ability of a model. In other words, to achieve high
generalization accuracy, do we really need all the samples in a training
dataset? In this paper, we demonstrate that deep learning models such as
convolutional neural networks may not favor all training samples, and
generalization accuracy can be further improved by dropping those unfavorable
samples. Specifically, the influence of removing a training sample is
quantifiable, and we propose a Two-Round Training approach, aiming to achieve
higher generalization accuracy. We locate unfavorable samples after the first
round of training, and then retrain the model from scratch with the reduced
training dataset in the second round. Since our approach is essentially
different from fine-tuning or further training, the computational cost should
not be a concern. Our extensive experimental results indicate that, with
identical settings, the proposed approach can boost performance of the
well-known networks on both high-level computer vision problems such as image
classification, and low-level vision problems such as image denoising
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Deep neural networks (DNN) have been shown to be useful in a wide range of
applications. However, they are also known to be vulnerable to adversarial
samples. By transforming a normal sample with some carefully crafted human
imperceptible perturbations, even highly accurate DNN make wrong decisions.
Multiple defense mechanisms have been proposed which aim to hinder the
generation of such adversarial samples. However, a recent work show that most
of them are ineffective. In this work, we propose an alternative approach to
detect adversarial samples at runtime. Our main observation is that adversarial
samples are much more sensitive than normal samples if we impose random
mutations on the DNN. We thus first propose a measure of `sensitivity' and show
empirically that normal samples and adversarial samples have distinguishable
sensitivity. We then integrate statistical hypothesis testing and model
mutation testing to check whether an input sample is likely to be normal or
adversarial at runtime by measuring its sensitivity. We evaluated our approach
on the MNIST and CIFAR10 datasets. The results show that our approach detects
adversarial samples generated by state-of-the-art attacking methods efficiently
and accurately.Comment: Accepted by ICSE 201
- …