36 research outputs found
Improving Acne Image Grading with Label Distribution Smoothing
Acne, a prevalent skin condition, necessitates precise severity assessment
for effective treatment. Acne severity grading typically involves lesion
counting and global assessment. However, manual grading suffers from
variability and inefficiency, highlighting the need for automated tools.
Recently, label distribution learning (LDL) was proposed as an effective
framework for acne image grading, but its effectiveness is hindered by severity
scales that assign varying numbers of lesions to different severity grades.
Addressing these limitations, we proposed to incorporate severity scale
information into lesion counting by combining LDL with label smoothing, and to
decouple if from global assessment. A novel weighting scheme in our approach
adjusts the degree of label smoothing based on the severity grading scale. This
method helped to effectively manage label uncertainty without compromising
class distinctiveness. Applied to the benchmark ACNE04 dataset, our model
demonstrated improved performance in automated acne grading, showcasing its
potential in enhancing acne diagnostics. The source code is publicly available
at http://github.com/openface-io/acne-lds.Comment: Accepted to IEEE ISBI 202
Deep Learning for Automatic Pneumonia Detection
Pneumonia is the leading cause of death among young children and one of the
top mortality causes worldwide. The pneumonia detection is usually performed
through examine of chest X-ray radiograph by highly-trained specialists. This
process is tedious and often leads to a disagreement between radiologists.
Computer-aided diagnosis systems showed the potential for improving diagnostic
accuracy. In this work, we develop the computational approach for pneumonia
regions detection based on single-shot detectors, squeeze-and-excitation deep
convolution neural networks, augmentations and multi-task learning. The
proposed approach was evaluated in the context of the Radiological Society of
North America Pneumonia Detection Challenge, achieving one of the best results
in the challenge.Comment: to appear in CVPR 2020 Workshops proceeding
FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations
Neural network-based methods for image processing are becoming widely used in
practical applications. Modern neural networks are computationally expensive
and require specialized hardware, such as graphics processing units. Since such
hardware is not always available in real life applications, there is a
compelling need for the design of neural networks for mobile devices. Mobile
neural networks typically have reduced number of parameters and require a
relatively small number of arithmetic operations. However, they usually still
are executed at the software level and use floating-point calculations. The use
of mobile networks without further optimization may not provide sufficient
performance when high processing speed is required, for example, in real-time
video processing (30 frames per second). In this study, we suggest
optimizations to speed up computations in order to efficiently use already
trained neural networks on a mobile device. Specifically, we propose an
approach for speeding up neural networks by moving computation from software to
hardware and by using fixed-point calculations instead of floating-point. We
propose a number of methods for neural network architecture design to improve
the performance with fixed-point calculations. We also show an example of how
existing datasets can be modified and adapted for the recognition task in hand.
Finally, we present the design and the implementation of a floating-point gate
array-based device to solve the practical problem of real-time handwritten
digit classification from mobile camera video feed
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Accurate detection and localization for angiodysplasia lesions is an
important problem in early stage diagnostics of gastrointestinal bleeding and
anemia. Gold-standard for angiodysplasia detection and localization is
performed using wireless capsule endoscopy. This pill-like device is able to
produce thousand of high enough resolution images during one passage through
gastrointestinal tract. In this paper we present our winning solution for
MICCAI 2017 Endoscopic Vision SubChallenge: Angiodysplasia Detection and
Localization its further improvements over the state-of-the-art results using
several novel deep neural network architectures. It address the binary
segmentation problem, where every pixel in an image is labeled as an
angiodysplasia lesions or background. Then, we analyze connected component of
each predicted mask. Based on the analysis we developed a classifier that
predict angiodysplasia lesions (binary variable) and a detector for their
localization (center of a component). In this setting, our approach outperforms
other methods in every task subcategory for angiodysplasia detection and
localization thereby providing state-of-the-art results for these problems. The
source code for our solution is made publicly available at
https://github.com/ternaus/angiodysplasia-segmentatioComment: 12 pages, 6 figure
Hypothesis: Caco‐2 cell rotational 3D mechanogenomic turing patterns have clinical implications to colon crypts
Colon crypts are recognized as a mechanical and biochemical Turing patterning model. Colon epithelial Caco‐2 cell monolayer demonstrated 2D Turing patterns via force analysis of apical tight junction live cell imaging which illuminated actomyosin meshwork linking the actomyosin network of individual cells. Actomyosin forces act in a mechanobiological manner that alters cell/nucleus/tissue morphology. We observed the rotational motion of the nucleus in Caco‐2 cells that appears to be driven by actomyosin during the formation of a differentiated confluent epithelium. Single‐ to multi‐cell ring/torus‐shaped genomes were observed prior to complex fractal Turing patterns extending from a rotating torus centre in a spiral pattern consistent with a gene morphogen motif. These features may contribute to the well‐described differentiation from stem cells at the crypt base to the luminal colon epithelium along the crypt axis. This observation may be useful to study the role of mechanogenomic processes and the underlying molecular mechanisms as determinants of cellular and tissue architecture in space and time, which is the focal point of the 4D nucleome initiative. Mathematical and bioengineer modelling of gene circuits and cell shapes may provide a powerful algorithm that will contribute to future precision medicine relevant to a number of common medical disorders.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146665/1/jcmm13853.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146665/2/jcmm13853_am.pd
Reproducible image-based profiling with Pycytominer
Technological advances in high-throughput microscopy have facilitated the
acquisition of cell images at a rapid pace, and data pipelines can now extract
and process thousands of image-based features from microscopy images. These
features represent valuable single-cell phenotypes that contain information
about cell state and biological processes. The use of these features for
biological discovery is known as image-based or morphological profiling.
However, these raw features need processing before use and image-based
profiling lacks scalable and reproducible open-source software. Inconsistent
processing across studies makes it difficult to compare datasets and processing
steps, further delaying the development of optimal pipelines, methods, and
analyses. To address these issues, we present Pycytominer, an open-source
software package with a vibrant community that establishes an image-based
profiling standard. Pycytominer has a simple, user-friendly Application
Programming Interface (API) that implements image-based profiling functions for
processing high-dimensional morphological features extracted from microscopy
images of cells. Establishing Pycytominer as a standard image-based profiling
toolkit ensures consistent data processing pipelines with data provenance,
therefore minimizing potential inconsistencies and enabling researchers to
confidently derive accurate conclusions and discover novel insights from their
data, thus driving progress in our field.Comment: 13 pages, 4 figure
SOCRAT: A Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization
Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data
Breast Tumor Cellularity Assessment Using Deep Neural Networks
© 2019 IEEE. Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment of tumor cellularity from post-treated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohen's kappa coefficient of 0.69 (vs. 0.42 previously known in literature) and the intra-class correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment