Search Results for author:
Found 7 papers, 1 papers with code
Segmentation of Kidney Tumors on Non-Contrast CT Images using Protuberance Detection Network
In this paper, we present a novel framework, which can explicitly capture protruded regions in kidneys to enable a better segmentation of kidney tumors.
Visual Grounding of Whole Radiology Reports for 3D CT Images
Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring.
C-SENN: Contrastive Self-Explaining Neural Network
In this study, we use a self-explaining neural network (SENN), which learns unsupervised concepts, to acquire concepts that are easy for people to understand automatically.
Concept Bottleneck Model with Additional Unsupervised Concepts
We refer to the proposed model as the concept bottleneck model with additional unsupervised concepts (CBM-AUC).
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation
We propose a novel reinforcement learning method with a reconstructor to improve the clinical correctness of generated reports to train the data-to-text module with a highly imbalanced dataset.
Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks
The input to the second networks have an auxiliary channel in addition to the 3D CT images.
Deep learning-based topological optimization for representing a user-specified design area
In this study, we propose a new deep learning model to generate an optimized structure for a given design domain and other boundary conditions without iteration.
