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Found 17 papers, 1 papers with code
Adaloss: Adaptive Loss Function for Landmark Localization
Landmark localization is a challenging problem in computer vision with a multitude of applications.
Ranked #8 on
Facial Landmark Detection
on 300W
Generating Synthetic X-ray Images of a Person from the Surface Geometry
Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.
Structure-Aware Shape Synthesis
We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function.
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available.
Deep Decision Network for Multi-Class Image Classification
During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks.
3D Organ Shape Reconstruction from Topogram Images
Automatic delineation and measurement of main organs such as liver is one of the critical steps for assessment of hepatic diseases, planning and postoperative or treatment follow-up.
View Invariant Human Body Detection and Pose Estimation from Multiple Depth Sensors
Point cloud based methods have produced promising results in areas such as 3D object detection in autonomous driving.
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
Lithography simulation is a critical step in VLSI design and optimization for manufacturability.
Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training
Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability, and so on.
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction
The emerging field of action prediction plays a vital role in various computer vision applications such as autonomous driving, activity analysis and human-computer interaction.
Automated CT Lung Cancer Screening Workflow using 3D Camera
We demonstrate the effectiveness of our method on a testing set of 110 pairs of depth data and CT scan, resulting in an average error of 5mm in estimating the isocenter, 13mm in determining the scan range, 10mm and 16mm in estimating the AP and lateral WED respectively.
EEG-Based Reaction Time Prediction with Fuzzy Common Spatial Patterns and Phase Cohesion using Deep Autoencoder Based Data Fusion
Drowsiness state of a driver is a topic of extensive discussion due to its significant role in causing traffic accidents.
Towards Establishing Dense Correspondence on Multiview Coronary Angiography: From Point-to-Point to Curve-to-Curve Query Matching
Coronary angiography is the gold standard imaging technique for studying and diagnosing coronary artery disease.
Cardiac ultrasound simulation for autonomous ultrasound navigation
However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies.
Feature boosting with efficient attention for scene parsing
This paper presents a novel feature-boosting network that gathers spatial context from multiple levels of feature extraction and computes the attention weights for each level of representation to generate the final class labels.
Toward Safe Evolution of Artificial Intelligence (AI) based Conversational Agents to Support Adolescent Mental and Sexual Health Knowledge Discovery
Following the recent release of various Artificial Intelligence (AI) based Conversation Agents (CAs), adolescents are increasingly using CAs for interactive knowledge discovery on sensitive topics, including mental and sexual health topics.
Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data Annotation
Therefore, we provide some of the early benefits and challenges of using LLMs-based tools for risk annotation and suggest future directions for the HCI research community to leverage LLMs as research tools to facilitate human-AI collaboration in contextualized online data annotation.
