AIMC Topic: Supervised Machine Learning

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Instance-Wise MRI Reconstruction Based on Self-Supervised Implicit Neural Representation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-s...

A Three-Stage Semi-Supervised Learning Approach to Spine Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Spine segmentation in computed tomography (CT) images is critical for automatic analysis, especially when focusing on varied spinal anatomy. Despite having comprehensive annotations for normal vertebrae, many datasets do not encompass labeled fractur...

UPMatch: Enhancing Semi-Supervised Medical Image Classification through Contrastive Learning with Unreliable Pseudo Labels.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pseudo-labeling based semi-supervised learning (SSL) framework has proven highly successful in medical image analysis (MIA) by addressing the problem of a shortage of labeled samples. However, the existing SSL methods use a fixed or flexible confiden...

Weakly Supervised Deep Learning in Radiology.

Radiology
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard...

Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.

Neural computation
In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local infor...

Supervised learning of enhancer-promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to le...

DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations.

Database : the journal of biological databases and curation
While biomedical relation extraction (bioRE) datasets have been instrumental in the development of methods to support biocuration of single variants from texts, no datasets are currently available for the extraction of digenic or even oligogenic vari...

Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction.

Briefings in bioinformatics
Self-supervised learning plays an important role in molecular representation learning because labeled molecular data are usually limited in many tasks, such as chemical property prediction and virtual screening. However, most existing molecular pre-t...

Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. Ho...

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Cell systems
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial tran...