Search Results for author:
Found 25 papers, 5 papers with code
A Diffusion-Based Framework for Occluded Object Movement
Seamlessly moving objects within a scene is a common requirement for image editing, but it is still a challenge for existing editing methods.
Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing
Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance.
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
Here, we present an active learning framework that combines crystal generation models and foundation atomic models to enhance the accuracy and efficiency of inverse design.
FaceMe: Robust Blind Face Restoration with Personal Identification
In this paper, we propose a personalized face restoration method, FaceMe, based on a diffusion model.
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients
We started by selecting serological indicators that significantly correlate with clinical outcomes and disease severity to serve as input data for the model.
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
The human brain receives nutrients and oxygen through an intricate network of blood vessels.
IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection
As large language models (LLMs) continue to advance, instruction tuning has become critical for improving their ability to generate accurate and contextually appropriate responses.
Realigning Confidence with Temporal Saliency Information for Point-Level Weakly-Supervised Temporal Action Localization
In CSL we design a novel center label generated by the point annotations for predicting aligned center scores.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
A comprehensive survey on deep active learning in medical image analysis
Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets.
TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction
The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD.
State estimation of a carbon capture process through POD model reduction and neural network approximation
Multi-layer perceptron (MLP) neural networks capture the dominant dynamics of the process and train the network parameters with low-dimensional data obtained from open-loop simulations.
Sensor network design for post-combustion CO2 capture plants: economy, complexity and robustness
The sensor selection problem is converted to an optimization problem, and is efficiently solved by a one-by-one removal approach through sensitivity analysis.
Explainable Semantic Medical Image Segmentation with Style
The discriminator is generalised to small domain shifts as much as permissible by the training data, and the generator automatically diversifies the training samples using a manifold of input features learnt during segmentation.
Advisory Tool for Managing Failure Cascades in Systems with Wind Power
This paper concerns the resilience of systems with wind power upon wind reduction by evaluating the potential of corrective actions, such as generation and load dispatch, on minimizing the effects of transmission line failures.
Towards Statistical Methods for Minimizing Effects of Failure Cascades
This paper concerns the potential of corrective actions, such as generation and load dispatch on minimizing the effects of transmission line failures in electric power systems.
Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data
The model uses a novel Cascaded Modality Transformer architecture with cross-attention to incorporate multi-modal information for more informed predictions.
Structure Guided Manifolds for Discovery of Disease Characteristics
DiDiGAN learns a disease manifold of AD and CN visual characteristics, and the style codes sampled from this manifold are imposed onto an anatomical structural "blueprint" to synthesise paired AD and CN magnetic resonance images (MRIs).
Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis
Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome.
A sensitivity-based approach to optimal sensor selection for process networks
The minimum number of sensors is determined in a way such that the local sensitivity matrix is full column rank.
Explainable AI for Suicide Risk Assessment Using Eye Activities and Head Gestures
The prevalence of suicide has been on the rise since the 20th century, causing severe emotional damage to individuals, families, and communities alike.
CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation
Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of individual objects with complex geometries within a large volume under investigation.
Manipulating Medical Image Translation with Manifold Disentanglement
In this work, we propose Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image translation framework that explicitly models these two types of features.
Generalisable 3D Fabric Architecture for Streamlined Universal Multi-Dataset Medical Image Segmentation
However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging.
Modeling the Control of COVID-19: Impact of Policy Interventions and Meteorological Factors
Home quarantine is the most important one to prevent the spread of COVID-19.
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN
We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention.
