Hosted on MSN

Mastering machine learning from code to tuning

From implementing KNN, PCA, and clustering to applying deep learning and scientific tuning, these resources show how to build, refine, and optimize machine learning models. They combine hands-on ...
Hosted on MSN

Turn your data into marketing wins with Python

From tracking brand mentions to predicting customer churn, Python is helping marketers connect analytics with measurable business outcomes. With structured methods for gathering, cleaning, and ...
IEEE

Enhancing Continual Semantic Segmentation via Uncertainty and Class Balance Re-Weighting

Abstract: Continual Semantic Segmentation (CSS) primarily aims to continually learn new semantic segmentation categories while avoiding catastrophic forgetting. In semantic segmentation tasks, images ...
The Lancet

End-to-end integrative segmentation and radiomics prognostic models for risk stratification of high-grade serous ovarian cancer: a retrospective multicohort study

iDepartment of Radiotherapy, The Royal Marsden NHS Foundation Trust, London, UK jLung Unit, The Royal Marsden NHS Foundation Trust, London, UK ...
GitHub

SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation

SANSA unlocks the hidden semantics of Segment Anything 2, turning it into a powerful few-shot segmenter for both objects and parts. 🚀 No fine-tuning of SAM2 weights. 🧠️ Fully promptable: points · ...
IEEE

Segment Any Cell: A SAM-Based Auto-Prompting Fine-Tuning Framework for Nuclei Segmentation

Abstract: In the rapidly evolving field of AI research, foundational models like BERT and GPT have significantly advanced language and vision tasks. The advent of pretrain-prompting models, such as ...