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Supervised Machine Learning

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Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

PeerJ
Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, ., can retard their growth. With a...

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.

Medical image analysis
Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for ...

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.

Journal of pharmaceutical sciences
Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytic...

SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised imag...

A robust self-supervised image hashing method for content identification with forensic detection of content-preserving manipulations.

Neural networks : the official journal of the International Neural Network Society
Image content identification systems have many applications in industry and academia. In particular, a hash-based content identification system uses a robust image hashing function that computes a short binary identifier summarizing the perceptual co...

COSST: Multi-Organ Segmentation With Partially Labeled Datasets Using Comprehensive Supervisions and Self-Training.

IEEE transactions on medical imaging
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially la...

Deep Omni-Supervised Learning for Rib Fracture Detection From Chest Radiology Images.

IEEE transactions on medical imaging
Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in preventing mortality and improving patient outcome. Normally, developing DL-based object detection models requires a huge amount of bounding box annotat...

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation.

IEEE transactions on medical imaging
Massive high-quality annotated data is required by fully-supervised learning, which is difficult to obtain for image segmentation since the pixel-level annotation is expensive, especially for medical image segmentation tasks that need domain knowledg...

A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.

PloS one
The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to pre...

Any region can be perceived equally and effectively on rotation pretext task using full rotation and weighted-region mixture.

Neural networks : the official journal of the International Neural Network Society
In recent years, self-supervised learning has emerged as a powerful approach to learning visual representations without requiring extensive manual annotation. One popular technique involves using rotation transformations of images, which provide a cl...