104 research outputs found
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated
pathological images, can alleviate the risk of overlooking the diagnosis.
Unfortunately, in medical imaging, most available datasets are
small/fragmented. To tackle this, as a Data Augmentation (DA) method, 3D
conditional Generative Adversarial Networks (GANs) can synthesize desired
realistic/diverse 3D images as additional training data. However, no 3D
conditional GAN-based DA approach exists for general bounding box-based 3D
object detection, while it can locate disease areas with physicians' minimum
annotation cost, unlike rigorous 3D segmentation. Moreover, since lesions vary
in position/size/attenuation, further GAN-based DA performance requires
multiple conditions. Therefore, we propose 3D Multi-Conditional GAN (MCGAN) to
generate realistic/diverse 32 X 32 X 32 nodules placed naturally on lung
Computed Tomography images to boost sensitivity in 3D object detection. Our
MCGAN adopts two discriminators for conditioning: the context discriminator
learns to classify real vs synthetic nodule/surrounding pairs with noise
box-centered surroundings; the nodule discriminator attempts to classify real
vs synthetic nodules with size/attenuation conditions. The results show that 3D
Convolutional Neural Network-based detection can achieve higher sensitivity
under any nodule size/attenuation at fixed False Positive rates and overcome
the medical data paucity with the MCGAN-generated realistic nodules---even
expert physicians fail to distinguish them from the real ones in Visual Turing
Test.Comment: 9 pages, 6 figures, accepted to 3DV 201
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
Feature Extraction and Design in Deep Learning Models
The selection and computation of meaningful features is critical for developing good deep learning methods. This dissertation demonstrates how focusing on this process can significantly improve the results of learning-based approaches. Specifically, this dissertation presents a series of different studies in which feature extraction and design was a significant factor for obtaining effective results. The first two studies are a content-based image retrieval system (CBIR) and a seagrass quantification study in which deep learning models were used to extract meaningful high-level features that significantly increased the performance of the approaches. Secondly, a method for change detection is proposed where the multispectral channels of satellite images are combined with different feature indices to improve the results. Then, two novel feature operators for mesh convolutional networks are presented that successfully extract invariant features from the faces and vertices of a mesh, respectively. The novel feature operators significantly outperform the previous state of the art for mesh classification and segmentation and provide two novel architectures for applying convolutional operations to the faces and vertices of geometric 3D meshes. Finally, a novel approach for automatic generation of 3D meshes is presented. The generative model efficiently uses the vertex-based feature operators proposed in the previous study and successfully learns to produce shapes from a mesh dataset with arbitrary topology
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future
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