16 citations · 36 across the 8 of their papers we have counts for
8 papers
Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers
Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi +2
Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors during radiotherapy, as treatment-system latency entails target-location uncertainties. This work ad…
Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training
Xingyu Song, Zhan Li, Shi Chen +1
3D human pose estimation is a vital task in computer vision, involving the prediction of human joint positions from images or videos to reconstruct a skeleton of a human in three-d…
An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
Xingyu Song, Zhan Li, Shi Chen +2
Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Netwo…
Real-time respiratory motion forecasting with online learning of recurrent neural networks for accurate targeting in externally guided radiotherapy
Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi +2
In lung radiotherapy, infrared cameras can track reflective objects on the chest to estimate tumor motion due to breathing, but treatment system latencies hinder radiation beam pre…
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition
Xingyu Song, Zhan Li, Shi Chen +1
Current datasets for action recognition tasks face limitations stemming from traditional collection and generation methods, including the constrained range of action classes, absen…
Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy
Michel Pohl, Mitsuru Uesaka, Kazuyuki Demachi +1
During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of resp…
Cross-Task Consistency Learning Framework for Multi-Task Learning
Akihiro Nakano, Shi Chen, Kazuyuki Demachi
Multi-task learning (MTL) is an active field in deep learning in which we train a model to jointly learn multiple tasks by exploiting relationships between the tasks. It has been s…
Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy
Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi +2
During lung radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tumor location. However, radiotherapy systems have a latency inhe…