1 citations · 1 across the 5 of their papers we have counts for
5 papers
Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Mayanka Chandrashekar, Xi Zhang, Ethan Seefried +3
Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At sc…
Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers
Olivera Kotevska, Ian Goethert, Michael McGee +11
Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage malignancies can be subtle o…
A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection
Julia Ive, Paulina Bondaronek, Vishal Yadav +10
Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on un…
Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population
Mayanka Chandrashekar, Ian Goethert, Md Inzamam Ul Haque +18
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic f…
CRL: Class Representative Learning for Image Classification
Mayanka Chandrashekar, Yugyung Lee
Building robust and real-time classifiers with diverse datasets are one of the most significant challenges to deep learning researchers. It is because there is a considerable gap b…