Search Results for author:
Found 45 papers, 26 papers with code
Time Matters: Examine Temporal Effects on Biomedical Language Models
Time roots in applying language models for biomedical applications: models are trained on historical data and will be deployed for new or future data, which may vary from training data.
Length-Aware Multi-Kernel Transformer for Long Document Classification
LAMKIT encodes lengthy documents by diverse transformer-based kernels for bridging context boundaries and vectorizes text length by the kernels to promote model robustness over varying document lengths.
Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts
To encounter those challenges, we introduce the Chain-of-Interaction (CoI) prompting method aiming to contextualize large language models (LLMs) for psychiatric decision support by the dyadic interactions.
Semantic-Aware Transformation-Invariant RoI Align
In this paper, we propose a novel RoI feature extractor, termed Semantic RoI Align (SRA), which is capable of extracting invariant RoI features under a variety of transformations for two-stage detectors.
Token Imbalance Adaptation for Radiology Report Generation
Experiments demonstrate the effectiveness of our approach in enhancing model robustness overall and infrequent tokens.
Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment
We collected a new clinical IMV dataset with GMA annotations, and our experiments show that SPN models for body parsing and pose estimation trained on the first two datasets generalize well to the new clinical dataset and their results can significantly boost the CRNN-based GMA prediction performance.
End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement
In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence.
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation.
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation
We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel.
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification
Unsupervised domain adaptation (UDA) augments model performance with only accessible annotations from the source domain and unlabeled data from the target domain.
Learning to Adapt Domain Shifts of Moral Values via Instance Weighting
In this study, we examine domain shifts of moral values and language usage, quantify the effects of domain shifts on the morality classification task, and propose a neural adaptation framework via instance weighting to improve cross-domain classification tasks.
Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
Existing approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined.
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
The semantic plane detection branch is based on a single-view plane detection framework but with differences.
Enriching Unsupervised User Embedding via Medical Concepts
Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection.
Deep Depth from Focus with Differential Focus Volume
Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera.
A Multi-attribute Controllable Generative Model for Histopathology Image Synthesis
Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks.
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Histopathological analysis is the present gold standard for precancerous lesion diagnosis.
DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning
In an emergency room (ER) setting, stroke triage or screening is a common challenge.
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives
In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have a computer system automatically generate photo-realistic images that adhere to the input content.
LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images
Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images.
Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff
In this paper we propose $\epsilon$-Consistent Mixup ($\epsilon$mu).
User Factor Adaptation for User Embedding via Multitask Learning
In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.
A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation
Rating prediction is a core problem in recommender systems to quantify user's preferences towards items, however, rating imbalance naturally roots in real-world user ratings that cause biased predictions and lead to poor performance on tail ratings.
Synthetic Sample Selection via Reinforcement Learning
However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images.
SiamParseNet: Joint Body Parsing and Label Propagation in Infant Movement Videos
In this paper, we propose a semi-supervised body parsing model, termed SiamParseNet (SPN), to jointly learn single frame body parsing and label propagation between frames in a semi-supervised fashion.
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes.
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.
Selective Synthetic Augmentation with Quality Assurance
To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.
Neural Wireframe Renderer: Learning Wireframe to Image Translations
To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes.
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
While recent work has shown promising results on cross-lingual transfer from high-resource languages to low-resource languages, it is unclear what knowledge is transferred.
Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification
Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations.
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models
Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations.
Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics
Language use varies across different demographic factors, such as gender, age, and geographic location.
Modeling Temporality of Human Intentions by Domain Adaptation
Categorizing patient{'}s intentions in conversational assessment can help decision making in clinical treatments.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Ranked #1 on
Text-to-Image Generation
on MS-COCO
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
Ranked #6 on
Text-to-Image Generation
on Oxford 102 Flowers
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
Ranked #4 on
Text-to-Image Generation
on Oxford 102 Flowers
(Inception score metric)
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDA-CNN) for fine-grained classification.
Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users
If people with high risk of suicide can be identified through social media like microblog, it is possible to implement an active intervention system to save their lives.
Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons
Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths. We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model.
