38 research outputs found
Brain Abnormality Detection by Deep Convolutional Neural Network
In this paper, we describe our method for classification of brain magnetic
resonance (MR) images into different abnormalities and healthy classes based on
the deep neural network. We propose our method to detect high and low-grade
glioma, multiple sclerosis, and Alzheimer diseases as well as healthy cases.
Our network architecture has ten learning layers that include seven
convolutional layers and three fully connected layers. We have achieved a
promising result in five categories of brain images (classification task) with
95.7% accuracy.Comment: Accepted for presenting in ACM-womENcourage_201
STN-OCR: A single Neural Network for Text Detection and Text Recognition
Detecting and recognizing text in natural scene images is a challenging, yet
not completely solved task. In re- cent years several new systems that try to
solve at least one of the two sub-tasks (text detection and text recognition)
have been proposed. In this paper we present STN-OCR, a step towards
semi-supervised neural networks for scene text recognition, that can be
optimized end-to-end. In contrast to most existing works that consist of
multiple deep neural networks and several pre-processing steps we propose to
use a single deep neural network that learns to detect and recognize text from
natural images in a semi-supervised way. STN-OCR is a network that integrates
and jointly learns a spatial transformer network, that can learn to detect text
regions in an image, and a text recognition network that takes the identified
text regions and recognizes their textual content. We investigate how our model
behaves on a range of different tasks (detection and recognition of characters,
and lines of text). Experimental results on public benchmark datasets show the
ability of our model to handle a variety of different tasks, without
substantial changes in its overall network structure.Comment: 9 pages, 6 figure
Deep Learning for Medical Image Analysis
This report describes my research activities in the Hasso Plattner Institute
and summarizes my Ph.D. plan and several novels, end-to-end trainable
approaches for analyzing medical images using deep learning algorithm. In this
report, as an example, we explore different novel methods based on deep
learning for brain abnormality detection, recognition, and segmentation. This
report prepared for the doctoral consortium in the AIME-2017 conference.Comment: Presented in doctoral consortium in the AIME-2017 conferenc
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
We propose a new generative adversarial architecture to mitigate imbalance
data problem in medical image semantic segmentation where the majority of
pixels belongs to a healthy region and few belong to lesion or non-health
region. A model trained with imbalanced data tends to bias toward healthy data
which is not desired in clinical applications and predicted outputs by these
networks have high precision and low sensitivity. We propose a new conditional
generative refinement network with three components: a generative, a
discriminative, and a refinement network to mitigate unbalanced data problem
through ensemble learning. The generative network learns to a segment at the
pixel level by getting feedback from the discriminative network according to
the true positive and true negative maps. On the other hand, the refinement
network learns to predict the false positive and the false negative masks
produced by the generative network that has significant value, especially in
medical application. The final semantic segmentation masks are then composed by
the output of the three networks. The proposed architecture shows
state-of-the-art results on LiTS-2017 for liver lesion segmentation, and two
microscopic cell segmentation datasets MDA231, PhC-HeLa. We have achieved
competitive results on BraTS-2017 for brain tumour segmentation
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Detecting and recognizing text in natural scene images is a challenging, yet
not completely solved task. In recent years several new systems that try to
solve at least one of the two sub-tasks (text detection and text recognition)
have been proposed. In this paper we present SEE, a step towards
semi-supervised neural networks for scene text detection and recognition, that
can be optimized end-to-end. Most existing works consist of multiple deep
neural networks and several pre-processing steps. In contrast to this, we
propose to use a single deep neural network, that learns to detect and
recognize text from natural images, in a semi-supervised way. SEE is a network
that integrates and jointly learns a spatial transformer network, which can
learn to detect text regions in an image, and a text recognition network that
takes the identified text regions and recognizes their textual content. We
introduce the idea behind our novel approach and show its feasibility, by
performing a range of experiments on standard benchmark datasets, where we
achieve competitive results.Comment: AAAI-18. arXiv admin note: substantial text overlap with
arXiv:1707.0883
Learning to Train a Binary Neural Network
Convolutional neural networks have achieved astonishing results in different
application areas. Various methods which allow us to use these models on mobile
and embedded devices have been proposed. Especially binary neural networks seem
to be a promising approach for these devices with low computational power.
However, understanding binary neural networks and training accurate models for
practical applications remains a challenge. In our work, we focus on increasing
our understanding of the training process and making it accessible to everyone.
We publish our code and models based on BMXNet for everyone to use. Within this
framework, we systematically evaluated different network architectures and
hyperparameters to provide useful insights on how to train a binary neural
network. Further, we present how we improved accuracy by increasing the number
of connections in the network.Comment: Code: https://github.com/Jopyth/BMXNe
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet
Binary Neural Networks (BNNs) can drastically reduce memory size and accesses
by applying bit-wise operations instead of standard arithmetic operations.
Therefore it could significantly improve the efficiency and lower the energy
consumption at runtime, which enables the application of state-of-the-art deep
learning models on low power devices. BMXNet is an open-source BNN library
based on MXNet, which supports both XNOR-Networks and Quantized Neural
Networks. The developed BNN layers can be seamlessly applied with other
standard library components and work in both GPU and CPU mode. BMXNet is
maintained and developed by the multimedia research group at Hasso Plattner
Institute and released under Apache license. Extensive experiments validate the
efficiency and effectiveness of our implementation. The BMXNet library, several
sample projects, and a collection of pre-trained binary deep models are
available for download at https://github.com/hpi-xnorComment: 4 page
Language Identification Using Deep Convolutional Recurrent Neural Networks
Language Identification (LID) systems are used to classify the spoken
language from a given audio sample and are typically the first step for many
spoken language processing tasks, such as Automatic Speech Recognition (ASR)
systems. Without automatic language detection, speech utterances cannot be
parsed correctly and grammar rules cannot be applied, causing subsequent speech
recognition steps to fail. We propose a LID system that solves the problem in
the image domain, rather than the audio domain. We use a hybrid Convolutional
Recurrent Neural Network (CRNN) that operates on spectrogram images of the
provided audio snippets. In extensive experiments we show, that our model is
applicable to a range of noisy scenarios and can easily be extended to
previously unknown languages, while maintaining its classification accuracy. We
release our code and a large scale training set for LID systems to the
community.Comment: to be presented at ICONIP 201
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination
We propose to tackle the mode collapse problem in generative adversarial
networks (GANs) by using multiple discriminators and assigning a different
portion of each minibatch, called microbatch, to each discriminator. We
gradually change each discriminator's task from distinguishing between real and
fake samples to discriminating samples coming from inside or outside its
assigned microbatch by using a diversity parameter . The generator is
then forced to promote variety in each minibatch to make the microbatch
discrimination harder to achieve by each discriminator. Thus, all models in our
framework benefit from having variety in the generated set to reduce their
respective losses. We show evidence that our solution promotes sample diversity
since early training stages on multiple datasets.Comment: WACV 202
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
We propose a new generative adversarial architecture to mitigate imbalance
data problem for the task of medical image semantic segmentation where the
majority of pixels belong to a healthy region and few belong to lesion or
non-health region. A model trained with imbalanced data tends to bias towards
healthy data which is not desired in clinical applications. We design a new
conditional GAN with two components: a generative model and a discriminative
model to mitigate imbalanced data problem through selective weighted loss.
While the generator is trained on sequential magnetic resonance images (MRI) to
learn semantic segmentation and disease classification, the discriminator
classifies whether a generated output is real or fake. The proposed
architecture achieved state-of-the-art results on ACDC-2017 for cardiac
segmentation and diseases classification. We have achieved competitive results
on BraTS-2017 for brain tumor segmentation and brain diseases classification.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.07216. arXiv admin note: text overlap with arXiv:1810.0387