21 citations · 31 across the 10 of their papers we have counts for
12 papers
Selective Prediction and Uncertainty-Aware Referral for Pap Smear Classification
Nisreen Albzour, Sarah S. Lam
Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed alongside a cytopathologist need n…
Reliability-Aware Ensemble Classification Under Class Imbalance: A Calibration Study on Liquid-Based Cervical Cytology
Nisreen Albzour, Sarah S. Lam
Cervical cytology classification models are typically evaluated on curated, class-balanced benchmarks, but real-world liquid-based cytology (LBC) collections are often small and cl…
Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
Nisreen Albzour, Sarah S. Lam
Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability. Although convolutional neura…
Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions
Sadaf Tabatabaee, Sarah S. Lam
Efficient classification of surgical procedures by urgency is paramount to optimize patient care and resource allocation within healthcare systems. This study introduces an unsuper…
Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning
Nisreen Albzour, Sarah S. Lam
Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women. Early detection through Pap smear tests is essential to reduce…
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification
Hajar Sakai, Sarah S. Lam
The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle the nuanced and complex nature of…
HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling
Hajar Sakai, Sarah S. Lam
Traditional topic models often struggle with contextual nuances and fail to adequately handle polysemy and rare words. This limitation typically results in topics that lack coheren…
Large Language Models for Healthcare Text Classification: A Systematic Review
Hajar Sakai, Sarah S. Lam
Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient…
QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification
Hajar Sakai, Sarah S. Lam
The escalating volume of collected healthcare textual data presents a unique challenge for automated Multi-Label Text Classification (MLTC), which is primarily due to the scarcity…
BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor
Mohammed-Khalil Ghali, Abdelrahman Farrag, Sarah Lam +1
Thematic analysis of social media posts provides a major understanding of public discourse, yet traditional methods often struggle to capture the complexity and nuance of unstructu…
Large Language Models for Patient Comments Multi-Label Classification
Hajar Sakai, Sarah S. Lam, Mohammadsadegh Mikaeili +2
Patient experience and care quality are crucial for a hospital's sustainability and reputation. The analysis of patient feedback offers valuable insight into patient satisfaction a…
GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models
Mohammed-Khalil Ghali, Abdelrahman Farrag, Hajar Sakai +3
In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical…