220 citations · 1.4k across the 42 of their papers we have counts for
44 papers
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification
Yizhe Zhang, Shuo Wang, Yejia Zhang +1
Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP p…
Edge-aware Feature Aggregation Network for Polyp Segmentation
Tao Zhou, Yizhe Zhang, Geng Chen +3
Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundar…
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
Yizhe Zhang, Tao Zhou, Shuo Wang +3
The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image seg…
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2
Diffusion models have demonstrated excellent potential for generating diverse images. However, their performance often suffers from slow generation due to iterative denoising. Know…
Can SAM Segment Polyps?
Tao Zhou, Yizhe Zhang, Yi Zhou +2
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentati…
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)
Haoran Wang, Yan Zhu, Wenzheng Qin +5
The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets…
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational au…
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
Peixian Liang, Yizhe Zhang, Yifan Ding +5
Deep learning (DL) based semantic segmentation methods have achieved excellent performance in biomedical image segmentation, producing high quality probability maps to allow extrac…
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Xiang Gao, Yizhe Zhang, Michel Galley +2
Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning m…
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
Xinjie Fan, Yizhe Zhang, Zhendong Wang +1
Sequence generation models are commonly refined with reinforcement learning over user-defined metrics. However, high gradient variance hinders the practical use of this method. To…
Contrastive Multi-document Question Generation
Woon Sang Cho, Yizhe Zhang, Sudha Rao +5
Multi-document question generation focuses on generating a question that covers the common aspect of multiple documents. Such a model is useful in generating clarifying options. Ho…
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang, Siqi Sun, Michel Galley +6
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
Woon Sang Cho, Yizhe Zhang, Sudha Rao +2
Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics. One potential solution is for the search engine to generate multiple…
Structuring Latent Spaces for Stylized Response Generation
Xiang Gao, Yizhe Zhang, Sungjin Lee +4
Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate response…
Second-order Non-local Attention Networks for Person Re-identification
Bryan, Xia, Yuan Gong +2
Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations fro…
Domain Adaptive Text Style Transfer
Dianqi Li, Yizhe Zhang, Zhe Gan +4
Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In t…
Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
Yizhe Zhang, Michael T. C. Ying, Danny Z. Chen
Segmentation maps of medical images annotated by medical experts contain rich spatial information. In this paper, we propose to decompose annotation maps to learn disentangled and…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Jointly Optimizing Diversity and Relevance in Neural Response Generation
Xiang Gao, Sungjin Lee, Yizhe Zhang +4
Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the…
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang +4
Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several…
Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen, Yizhe Zhang, Ruiyi Zhang +7
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word…
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
Peixian Liang, Jianxu Chen, Hao Zheng +3
The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and…
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
Hao Zheng, Yizhe Zhang, Lin Yang +4
3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomed…
Adversarial Text Generation via Feature-Mover's Distance
Liqun Chen, Shuyang Dai, Chenyang Tao +5
Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text…