Search Results for author:
Found 14 papers, 2 papers with code
Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI
Motivated by the need for advanced solutions in the segmentation and inpainting of glioma-affected brain regions in multi-modal magnetic resonance imaging (MRI), this study presents an integrated approach leveraging the strengths of ensemble learning with hybrid transformer models and convolutional neural networks (CNNs), alongside the innovative application of 3D Pix2Pix Generative Adversarial Network (GAN).
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors
This method captures spatial and contextual details from MRI data, fine-tuned on diverse datasets representing common tumor types.
LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions
Accurately determining the shape of objects and the location of their internal structures within deformable objects is crucial for medical tasks that require precise targeting, such as robotic biopsies.
Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks
To track tumors during surgery, information from preoperative CT scans is used to determine their position.
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images
We introduce a novel method employing occupancy networks for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body.
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions.
Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud
In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object.
Grounding Graph Network Simulators using Physical Sensor Observations
Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution
It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0. 9294, 0. 8788, and 0. 8803, and Hausdorf distance of 5. 23, 13. 54, and 12. 05, for the whole tumor, tumor core, and enhancing tumor, respectively.
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients
Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes.
LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes
The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions.
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI
Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #2 on
Surgical phase recognition
on HeiChole Benchmark
