641 research outputs found
A Deep DUAL-PATH Network for Improved Mammogram Image Processing
We present, for the first time, a novel deep neural network architecture
called \dcn with a dual-path connection between the input image and output
class label for mammogram image processing. This architecture is built upon
U-Net, which non-linearly maps the input data into a deep latent space. One
path of the \dcnn, the locality preserving learner, is devoted to
hierarchically extracting and exploiting intrinsic features of the input, while
the other path, called the conditional graph learner, focuses on modeling the
input-mask correlations. The learned mask is further used to improve
classification results, and the two learning paths complement each other. By
integrating the two learners our new architecture provides a simple but
effective way to jointly learn the segmentation and predict the class label.
Benefiting from the powerful expressive capacity of deep neural networks a more
discriminative representation can be learned, in which both the semantics and
structure are well preserved. Experimental results show that \dcn achieves the
best mammography segmentation and classification simultaneously, outperforming
recent state-of-the-art models.Comment: To Appear in ICCASP 2019 Ma
Deep Learning for Automated Medical Image Analysis
Medical imaging is an essential tool in many areas of medical applications,
used for both diagnosis and treatment. However, reading medical images and
making diagnosis or treatment recommendations require specially trained medical
specialists. The current practice of reading medical images is labor-intensive,
time-consuming, costly, and error-prone. It would be more desirable to have a
computer-aided system that can automatically make diagnosis and treatment
recommendations. Recent advances in deep learning enable us to rethink the ways
of clinician diagnosis based on medical images. In this thesis, we will
introduce 1) mammograms for detecting breast cancers, the most frequently
diagnosed solid cancer for U.S. women, 2) lung CT images for detecting lung
cancers, the most frequently diagnosed malignant cancer, and 3) head and neck
CT images for automated delineation of organs at risk in radiotherapy. First,
we will show how to employ the adversarial concept to generate the hard
examples improving mammogram mass segmentation. Second, we will demonstrate how
to use the weakly labeled data for the mammogram breast cancer diagnosis by
efficiently design deep learning for multi-instance learning. Third, the thesis
will walk through DeepLung system which combines deep 3D ConvNets and GBM for
automated lung nodule detection and classification. Fourth, we will show how to
use weakly labeled data to improve existing lung nodule detection system by
integrating deep learning with a probabilistic graphic model. Lastly, we will
demonstrate the AnatomyNet which is thousands of times faster and more accurate
than previous methods on automated anatomy segmentation.Comment: PhD Thesi
COIN:Contrastive Identifier Network for Breast Mass Diagnosis in Mammography
Computer-aided breast cancer diagnosis in mammography is a challenging
problem, stemming from mammographical data scarcity and data entanglement. In
particular, data scarcity is attributed to the privacy and expensive
annotation. And data entanglement is due to the high similarity between benign
and malignant masses, of which manifolds reside in lower dimensional space with
very small margin. To address these two challenges, we propose a deep learning
framework, named Contrastive Identifier Network (\textsc{COIN}), which
integrates adversarial augmentation and manifold-based contrastive learning.
Firstly, we employ adversarial learning to create both on- and off-distribution
mass contained ROIs. After that, we propose a novel contrastive loss with a
built Signed graph. Finally, the neural network is optimized in a contrastive
learning manner, with the purpose of improving the deep model's
discriminativity on the extended dataset. In particular, by employing COIN,
data samples from the same category are pulled close whereas those with
different labels are pushed further in the deep latent space. Moreover, COIN
outperforms the state-of-the-art related algorithms for solving breast cancer
diagnosis problem by a considerable margin, achieving 93.4\% accuracy and
95.0\% AUC score. The code will release on ***
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition
Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
In this study, the main objective is to develop an algorithm capable of
identifying and delineating tumor regions in breast ultrasound (BUS) and
mammographic images. The technique employs two advanced deep learning
architectures, namely U-Net and pretrained SAM, for tumor segmentation. The
U-Net model is specifically designed for medical image segmentation and
leverages its deep convolutional neural network framework to extract meaningful
features from input images. On the other hand, the pretrained SAM architecture
incorporates a mechanism to capture spatial dependencies and generate
segmentation results. Evaluation is conducted on a diverse dataset containing
annotated tumor regions in BUS and mammographic images, covering both benign
and malignant tumors. This dataset enables a comprehensive assessment of the
algorithm's performance across different tumor types. Results demonstrate that
the U-Net model outperforms the pretrained SAM architecture in accurately
identifying and segmenting tumor regions in both BUS and mammographic images.
The U-Net exhibits superior performance in challenging cases involving
irregular shapes, indistinct boundaries, and high tumor heterogeneity. In
contrast, the pretrained SAM architecture exhibits limitations in accurately
identifying tumor areas, particularly for malignant tumors and objects with
weak boundaries or complex shapes. These findings highlight the importance of
selecting appropriate deep learning architectures tailored for medical image
segmentation. The U-Net model showcases its potential as a robust and accurate
tool for tumor detection, while the pretrained SAM architecture suggests the
need for further improvements to enhance segmentation performance
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
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