1 citations · 2 across the 5 of their papers we have counts for
6 papers
DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment
Shiyu Teng, Haichen Yu, Jiaqing Liu +6
Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questio…
Dynamic Summary Generation for Interpretable Multimodal Depression Detection
Shiyu Teng, Jiaqing Liu, Hao Sun +6
Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this challenge, we propose a coarse-to…
Retrieval-Augmented Multimodal Depression Detection
Ruibo Hou, Shiyu Teng, Jiaqing Liu +4
Multimodal deep learning has shown promise in depression detection by integrating text, audio, and video signals. Recent work leverages sentiment analysis to enhance emotional unde…
A Text-Image Fusion Method with Data Augmentation Capabilities for Referring Medical Image Segmentation
Shurong Chai, Rahul Kumar JAIN, Rui Xu +6
Deep learning relies heavily on data augmentation to mitigate limited data, especially in medical imaging. Recent multimodal learning integrates text and images for segmentation, k…
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
Xiaoyu Shi, Rahul Kumar Jain, Yinhao Li +7
Deep learning has demonstrated remarkable success in medical image segmentation and computer-aided diagnosis. In particular, numerous advanced methods have achieved state-of-the-ar…
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
Shiyu Teng, Jiaqing Liu, Rahul Kumar Jain +5
Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Recent advancements in Large Langu…