Search Results for author:
Found 11 papers, 1 papers with code
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0. 01) between all classes.
Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images
In order to leverage the multi-magnification information and early fusion with graph convolutional networks, we handle different embedding spaces at each magnification by introducing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN) as a multiple instance learning method.
ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure
Deep learning, especially convolutional neural networks, has triggered accelerated advancements in computer vision, bringing changes into our daily practice.
Generalizable Neural Radiance Fields for Novel View Synthesis with Transformer
We propose a Transformer-based NeRF (TransNeRF) to learn a generic neural radiance field conditioned on observed-view images for the novel view synthesis task.
Multi-task UNet: Jointly Boosting Saliency Prediction and Disease Classification on Chest X-ray Images
To support the use of visual attention, this paper describes a novel deep learning model for visual saliency prediction on chest X-ray (CXR) images.
Gaze-Guided Class Activation Mapping: Leveraging Human Attention for Network Attention in Chest X-rays Classification
However, no previous research has combined the network attention and human attention.
Multifrequency 3D Elasticity Reconstruction withStructured Sparsity and ADMM
We evaluate our new method in multiple in silico and phantom experiments, with comparisons with existing methods, and we show improvements in contrast to noise and signal to noise ratios.
Automatic Segmentation of the Prostate on 3D Trans-rectal Ultrasound Images using Statistical Shape Models and Convolutional Neural Networks
In this work we propose to segment the prostate on a challenging dataset of trans-rectal ultrasound (TRUS) images using convolutional neural networks (CNNs) and statistical shape models (SSMs).
Multi-view 3D Reconstruction with Transformer
Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network.
Ranked #7 on
3D Reconstruction
on ShapeNet
Multi-View 3D Reconstruction With Transformers
Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network.
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging
A global CNN will determine a prostate bounding box, which is then resampled and sent to a local CNN for accurate delineation of the prostate boundary.
