Search Results for author:
Found 12 papers, 1 papers with code
Learning the irreversible progression trajectory of Alzheimer's disease
Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits.
Semi-weakly-supervised neural network training for medical image registration
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective.
BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision
These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera.
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.
A Unified Study of Machine Learning Explanation Evaluation Metrics
The growing need for trustworthy machine learning has led to the blossom of interpretability research.
Self-Interpretable Model with Transformation Equivariant Interpretation
With the proliferation of machine learning applications in the real world, the demand for explaining machine learning predictions continues to grow especially in high-stakes fields.
Self-Interpretable Model with TransformationEquivariant Interpretation
In this paper, we propose a self-interpretable model SITE with transformation-equivariant interpretations.
Learning Multi-level Features For Sensor-based Human Action Recognition
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance
In this paper, we propose an innovative end-to-end subtitle detection and recognition system for videos in East Asian languages.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
Gland Instance Segmentation by Deep Multichannel Side Supervision
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images.
