6,024 research outputs found
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets of
dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human
Against Machine with 10000 training images") dataset. We collected
dermatoscopic images from different populations acquired and stored by
different modalities. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy
Low-Rank Discriminative Least Squares Regression for Image Classification
Latest least squares regression (LSR) methods mainly try to learn slack
regression targets to replace strict zero-one labels. However, the difference
of intra-class targets can also be highlighted when enlarging the distance
between different classes, and roughly persuing relaxed targets may lead to the
problem of overfitting. To solve above problems, we propose a low-rank
discriminative least squares regression model (LRDLSR) for multi-class image
classification. Specifically, LRDLSR class-wisely imposes low-rank constraint
on the intra-class regression targets to encourage its compactness and
similarity. Moreover, LRDLSR introduces an additional regularization term on
the learned targets to avoid the problem of overfitting. These two improvements
are helpful to learn a more discriminative projection for regression and thus
achieving better classification performance. Experimental results over a range
of image databases demonstrate the effectiveness of the proposed LRDLSR method
Nutrient Trading in the Chesapeake Bay Region: An Analysis of Supply and Demand
This report provides an overview of nutrient trading programs as they currently exist in the Chesapeake Bay watershed and examines the potential for supply and demand of credits within those markets. In addition, the analysis considers the potential impacts of Bay-wide Total Maximum Daily Loads on nutrient trading - particularly those on the agricultural sector's ability to generate credits
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