Search Results for author:
Found 19 papers, 5 papers with code
Tell2Reg: Establishing spatial correspondence between images by the same language prompts
Spatial correspondence can be represented by pairs of segmented regions, such that the image registration networks aim to segment corresponding regions rather than predicting displacement fields or transformation parameters.
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement.
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging.
Students Rather Than Experts: A New AI For Education Pipeline To Model More Human-Like And Personalised Early Adolescences
Specifically, we: (1) develop a theoretical framework for generating LVSA; (2) integrate human subjective evaluation metrics into GPT-4 assessments, demonstrating a strong correlation between human evaluators and GPT-4 in judging LVSA authenticity; and (3) validate that LLMs can generate human-like, personalized virtual student agents in educational contexts, laying a foundation for future applications in pre-service teacher training and multi-agent simulation environments.
Can LVLMs Describe Videos like Humans? A Five-in-One Video Annotations Benchmark for Better Human-Machine Comparison
To address these issues, we propose a novel benchmark, FIOVA (Five In One Video Annotations), designed to evaluate the differences between LVLMs and human understanding more comprehensively.
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI
The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis.
LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Contemporary recommendation systems predominantly rely on ID embedding to capture latent associations among users and items.
Learning the irreversible progression trajectory of Alzheimer's disease
Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits.
Semi-weakly-supervised neural network training for medical image registration
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective.
BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision
These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera.
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.
A Unified Study of Machine Learning Explanation Evaluation Metrics
The growing need for trustworthy machine learning has led to the blossom of interpretability research.
Self-Interpretable Model with Transformation Equivariant Interpretation
With the proliferation of machine learning applications in the real world, the demand for explaining machine learning predictions continues to grow especially in high-stakes fields.
Self-Interpretable Model with TransformationEquivariant Interpretation
In this paper, we propose a self-interpretable model SITE with transformation-equivariant interpretations.
Learning Multi-level Features For Sensor-based Human Action Recognition
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
In this paper, we propose an innovative end-to-end subtitle detection and recognition system for videos in East Asian languages.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
Gland Instance Segmentation by Deep Multichannel Side Supervision
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images.
