Search Results for author:
Found 9 papers, 7 papers with code
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation
Our findings demonstrate that: (1) strategically selecting a combination of synthetic tumor sizes is crucial for optimal segmentation outcomes, and (2) generating synthetic tumors with precise boundaries significantly improves model accuracy.
Pronunciation Assessment with Multi-modal Large Language Models
The assessment task-specific prefix and prompt text are embedded and concatenated with the features generated by the modality adapter layer, enabling the LLMs to predict accuracy and fluency scores.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
Spoken Language Intelligence of Large Language Models for Language Learning
Our focus is centered on evaluating the efficacy of LLMs in the realm of education, specifically in the areas of spoken language learning which encompass phonetics, phonology, and second language acquisition.
YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation
With only one noisy skeleton annotation (respectively 0. 14\%, 0. 03\%, 1. 40\%, and 0. 65\% of the full annotation), YoloCurvSeg achieves more than 97\% of the fully-supervised performance on each dataset.
Text-Aware End-to-end Mispronunciation Detection and Diagnosis
In the field of assessing the pronunciation quality of constrained speech, the given transcriptions can play the role of a teacher.
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student.
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.
