First, we pretrained the encoder of a transformer-based network using a self-supervised approach on unlabeled abdominal computed tomography images. Subsequently, we fine-tuned the segmentation network ...
Abstract: Overcoming class imbalance is a critical challenge for graph-based semi-supervised classification methods. In this letter, we address this issue from the perspective of graph filtering and ...
Quantifying natural behavior from video recordings is a key component in ethological studies. Markerless pose estimation methods have provided an important step toward that goal by automatically ...
ABSTRACT: Change-detection analysis highlighted significant declines in sparse forest (−72.88%) and wetlands (−73.49%), alongside a substantial increase in bare land (+55.26%). These trends underscore ...
Multi-view learning is gradually becoming a well-established domain within machine learning that tackles problems involving the availability of multiple views or sources of data. Existing multi-view ...
ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...
This notebook tested the performance of the following scikit-learn models: Logistic Regression, Multilayer Perception, Naive Bayes, KNN, and Random Forest Classifier in classifying whether a person ...
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Teasing out biochemical information from ancient organic-rich sediments, notably the timing of the emergence of photosynthesis relative to the inferred oxygenation of Earth’s atmosphere, remains a ...
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