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Found 29 papers, 12 papers with code
Self-Supervised Learning Based Handwriting Verification
We present SSL-HV: Self-Supervised Learning approaches applied to the task of Handwriting Verification.
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
In this study, we propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups in high-dimensional chest X- ray multi-label classification.
Continual Domain Adversarial Adaptation via Double-Head Discriminators
We prove that with the introduction of a pre-trained source-only domain discriminator, the empirical estimation error of $\gH$-divergence related adversarial loss is reduced from the source domain side.
Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans
Deep learning empowers the mainstream medical image segmentation methods.
Continual Segment: Towards a Single, Unified and Non-forgetting Continual Segmentation Model of 143 Whole-body Organs in CT Scans
In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs.
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation
To make them trustworthy and robust to corruptions deployed in safety-critical scenarios, we propose a meta-learning framework of self-adaptive data augmentation to tackle the corruption robustness in CL.
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift.
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning
Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs).
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO).
A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning
Based on our particle filter inference algorithm, a semi-supervised learn-ing algorithm is utilized for learning tracking network on intermittent labeled frames by variational inference.
Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model
Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the distributed representation of the learned dynamics is hard to interpret.
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach
One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space.
Learning To Learn and Remember Super Long Multi-Domain Task Sequence
We propose a domain shift detection technique to capture latent domain change and equip the meta optimizer with it to work in this setting.
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model.
Few-shot Learning via Dirichlet Tessellation Ensemble
One observation is that the widely embraced ProtoNet model is essentially a Dirichlet Tessellation (Voronoi Diagram) in the feature space.
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
In this paper, we explore a more practical and challenging setting where task distribution changes over time with domain shift.
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision.
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation
Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks.
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation
Many segmentation tasks for biomedical images can be modeled as the minimization of an energy function and solved by a class of max-flow and min-cut optimization algorithms.
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their learning process.
Soft-Attention Improves Skin Cancer Classification Performance
Soft-Attention mechanism enables a neural network toachieve this goal.
Ranked #1 on
Lesion Classification
on ISIC 2017
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning
Our introduced SGLD oracle would lower generalization errors in local node's parameter learning and provide local node DP protections.
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation
In this paper, we use only two kinds of weak labels, i. e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions.
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment.
Ranked #2 on
Cardiovascular MR Segmentaiton
on HVSMR 2016
Brain Tumor Segmentation on MRI with Missing Modalities
We design a brain tumor segmentation algorithm that is robust to the absence of any modality.
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization
We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays.
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually delineated regions of interest (ROIs), is a clinically desirable, yet challenging goal.
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Both sets of features were combined to estimate the nodule malignancy using a random forest classifier.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Another effective method is transfer learning, i. e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks.
