most citedMachine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization

1 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2025

Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework

Mohammad R. Salmanpour, Sonya Falahati, Amir Hossein Pouria +10

Lung cancer remains the leading cause of cancer mortality, with CT imaging central to screening, prognosis, and treatment. Manual segmentation is variable and time-intensive, while…

cs.HC2025★ 1 cited

An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study

Sonya Falahati, Morteza Alizadeh, Fatemeh Ghazipour +3

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) med…

cs.LG2025

AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning

Morteza Alizadeh, Mehrdad Oveisi, Sonya Falahati +6

Machine learning (ML) models rely heavily on consistent and accurate performance metrics to evaluate and compare their effectiveness. However, existing libraries often suffer from…

cs.LG2024★ 1 cited

Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization

Mohammad R. Salmanpour, Morteza Alizadeh, Ghazal Mousavi +5

This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation.…