7 papers
AE-LLM: Adaptive Efficiency Optimization for Large Language Models
Kaito Tanaka, Masato Ito, Yuji Nishimura +2
Large Language Models (LLMs) have achieved remarkable success across diverse applications, yet their deployment remains challenging due to substantial computational costs, memory r…
Contextual Discrepancy-Aware Contrastive Learning for Robust Medical Time Series Diagnosis in Small-Sample Scenarios
Kaito Tanaka, Aya Nakayama, Masato Ito +2
Medical time series data, such as EEG and ECG, are vital for diagnosing neurological and cardiovascular diseases. However, their precise interpretation faces significant challenges…
Semantic-Preserving Cross-Style Visual Reasoning for Robust Multi-Modal Understanding in Large Vision-Language Models
Aya Nakayama, Brian Wong, Yuji Nishimura +1
The "style trap" poses a significant challenge for Large Vision-Language Models (LVLMs), hindering robust semantic understanding across diverse visual styles, especially in in-cont…
XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis
Masato Ito, Kaito Tanaka, Keisuke Matsuda +1
Diabetic Retinopathy (DR) is a major cause of global blindness, necessitating early and accurate diagnosis. While deep learning models have shown promise in DR detection, their bla…
High-Fidelity Pseudo-label Generation by Large Language Models for Training Robust Radiology Report Classifiers
Brian Wong, Kaito Tanaka
Automated labeling of chest X-ray reports is essential for enabling downstream tasks such as training image-based diagnostic models, population health studies, and clinical decisio…
Optimizing Vision-Language Interactions Through Decoder-Only Models
Kaito Tanaka, Benjamin Tan, Brian Wong
Vision-Language Models (VLMs) have emerged as key enablers for multimodal tasks, but their reliance on separate visual encoders introduces challenges in efficiency, scalability, an…