7,620 research outputs found
Experiential-Informed Data Reconstruction for Fishery Sustainability and Policies in the Azores
Fishery analysis is critical in maintaining the long-term sustainability of
species and the livelihoods of millions of people who depend on fishing for
food and income. The fishing gear, or metier, is a key factor significantly
impacting marine habitats, selectively targeting species and fish sizes.
Analysis of commercial catches or landings by metier in fishery stock
assessment and management is crucial, providing robust estimates of fishing
efforts and their impact on marine ecosystems. In this paper, we focus on a
unique data set from the Azores' fishing data collection programs between 2010
and 2017, where little information on metiers is available and sparse
throughout our timeline. Our main objective is to tackle the task of data set
reconstruction, leveraging domain knowledge and machine learning methods to
retrieve or associate metier-related information to each fish landing. We
empirically validate the feasibility of this task using a diverse set of
modeling approaches and demonstrate how it provides new insights into different
fisheries' behavior and the impact of metiers over time, which are essential
for future fish population assessments, management, and conservation efforts
Image Features for Tuberculosis Classification in Digital Chest Radiographs
Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very large, image features are used to describe the CXR image in a lower dimension while preserving the elements in the CXR necessary for the detection of TB. In this thesis, I present a set of image features using Pyramid Histogram of Oriented Gradients, Local Binary Patterns, and Principal Component Analysis which provides high classifier performance on two publicly available CXR datasets, and compare my results to current state-of-the-art research
Graduate Catalog, 1992-1993
https://scholar.valpo.edu/gradcatalogs/1020/thumbnail.jp
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins
Deploying off-the-shelf segmentation networks on biomedical data has become
common practice, yet if structures of interest in an image sequence are visible
only temporarily, existing frame-by-frame methods fail. In this paper, we
provide a solution to segmentation of imperfect data through time based on
temporal propagation and uncertainty estimation. We integrate uncertainty
estimation into Mask R-CNN network and propagate motion-corrected segmentation
masks from frames with low uncertainty to those frames with high uncertainty to
handle temporary loss of signal for segmentation. We demonstrate the value of
this approach over frame-by-frame segmentation and regular temporal propagation
on data from human embryonic kidney (HEK293T) cells transiently transfected
with a fluorescent protein that moves in and out of the nucleus over time. The
method presented here will empower microscopic experiments aimed at
understanding molecular and cellular function.Comment: Accepted at MICCAI Workshop on Medical Image Learning with Less
Labels and Imperfect Data, 202
Graduate Catalog, 1993-1994
https://scholar.valpo.edu/gradcatalogs/1021/thumbnail.jp
- …