Using a custom "camera-to-rice" platform combined with deep-learning methods for feature extraction, matching, segmentation, and denoising, the system ...

Master’s thesis position (M.Sc. student) in Deep Learning for Healthcare.

pyimcyto is a python library for nuclear and cellular segmentation in imaging mass cytometry images. The segmentation model uses a U-net++ archiecture with autoencoder-based anomaly detection to ...

This is a pipeline that uses deep learning techniques to parcel brain nuclei based on tractography. It includes the entire process from dataset construction to nuclei parcellation and performance ...

Dividing patients into groups based on how they behave towards their condition can aid understanding of the issues that affect them and improve outcomes, such as quality of life in long-term ...

Ann Behan has 10 years-plus of experience researching, writing, and editing articles, white papers, and executing searches at the board level across various industries. Her expertise includes ...

Analyzing stochastic cell-to-cell variability can potentially reveal causal interactions in gene regulatory networks.

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Abstract: Medical image segmentation and classification are two of the most key steps in computer-aided clinical diagnosis. The region of interest were usually segmented in a proper manner to extract ...

Leveraging the extensive training data from SA-1B, the segment anything model (SAM) demonstrates remarkable generalization and zero-shot capabilities. However, as a category-agnostic instance ...