AIMC Topic: Supervised Machine Learning

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Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study.

Frontiers in immunology
OBJECTIVE: Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunothera...

A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification.

Journal of imaging informatics in medicine
Diabetic retinopathy (DR) is a retinal disease caused by diabetes. If there is no intervention, it may even lead to blindness. Therefore, the detection of diabetic retinopathy is of great significance for preventing blindness in patients. Most of the...

Facial Action Unit Representation Based on Self-Supervised Learning With Ensembled Priori Constraints.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Facial action units (AUs) focus on a comprehensive set of atomic facial muscle movements for human expression understanding. Based on supervised learning, discriminative AU representation can be achieved from local patches where the AUs are located. ...

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

Nature protocols
Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. Ho...

Object and spatial discrimination makes weakly supervised local feature better.

Neural networks : the official journal of the International Neural Network Society
Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precis...

Semi-supervised machine learning method for predicting homogeneous ancestry groups to assess Hardy-Weinberg equilibrium in diverse whole-genome sequencing studies.

American journal of human genetics
Large-scale, multi-ethnic whole-genome sequencing (WGS) studies, such as the National Human Genome Research Institute Genome Sequencing Program's Centers for Common Disease Genomics (CCDG), play an important role in increasing diversity for genetic r...

Detecting floating litter in freshwater bodies with semi-supervised deep learning.

Water research
Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expen...

Prediction of individual weight loss using supervised learning: findings from the CALERIE 2 study.

The American journal of clinical nutrition
BACKGROUND: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual.

BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroe...

Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning.

Communications biology
The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-...