Search Results for author: Junjun He

Found 37 papers, 21 papers with code

OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine

no code implementations28 Feb 2024 Xiaosong Wang, Xiaofan Zhang, Guotai Wang, Junjun He, Zhongyu Li, Wentao Zhu, Yi Guo, Qi Dou, Xiaoxiao Li, Dequan Wang, Liang Hong, Qicheng Lao, Tong Ruan, Yukun Zhou, Yixue Li, Jie Zhao, Kang Li, Xin Sun, Lifeng Zhu, Shaoting Zhang

The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.

OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM

no code implementations14 Feb 2024 Yutao Hu, Tianbin Li, Quanfeng Lu, Wenqi Shao, Junjun He, Yu Qiao, Ping Luo

A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions, which is essential in real-world medical applications.

Medical Visual Question Answering Question Answering +1

Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey

no code implementations15 Dec 2023 Xu Liu, Tong Zhou, Yuanxin Wang, Yuping Wang, Qinjingwen Cao, Weizhi Du, Yonghuan Yang, Junjun He, Yu Qiao, Yiqing Shen

The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities.

Image Generation Image Segmentation +2

Universal Incomplete-View CT Reconstruction with Prompted Contextual Transformer

1 code implementation13 Dec 2023 Chenglong Ma, Zilong Li, Junjun He, Junping Zhang, Yi Zhang, Hongming Shan

The incomplete-view CT can be divided into two scenarios depending on the sampling of projection, sparse-view CT and limited-angle CT, each encompassing various settings for different clinical requirements.

Computed Tomography (CT)

SAM-Med3D

1 code implementation23 Oct 2023 Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Junlong Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, Shaoting Zhang, Junjun He, Yu Qiao

These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.

3D Architecture Image Segmentation +1

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

2 code implementations7 Sep 2023 Ziyan Huang, Zhongying Deng, Jin Ye, Haoyu Wang, Yanzhou Su, Tianbin Li, Hui Sun, Junlong Cheng, Jianpin Chen, Junjun He, Yun Gu, Shaoting Zhang, Lixu Gu, Yu Qiao

To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.

Organ Segmentation Segmentation

Artifact Restoration in Histology Images with Diffusion Probabilistic Models

1 code implementation26 Jul 2023 Zhenqi He, Junjun He, Jin Ye, Yiqing Shen

Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD) systems.

Denoising whole slide images

Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation

1 code implementation22 Jul 2023 Yuncheng Yang, Meng Wei, Junjun He, Jie Yang, Jin Ye, Yun Gu

To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method.

Image Segmentation Medical Image Segmentation +3

Learning with Explicit Shape Priors for Medical Image Segmentation

1 code implementation31 Mar 2023 Xin You, Junjun He, Jie Yang, Yun Gu

Hence, in our work, we proposed a novel shape prior module (SPM), which can explicitly introduce shape priors to promote the segmentation performance of UNet-based models.

Image Segmentation Medical Image Segmentation +2

Token Sparsification for Faster Medical Image Segmentation

1 code implementation11 Mar 2023 Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna

To this end, we reformulate segmentation as a sparse encoding -> token completion -> dense decoding (SCD) pipeline.

Image Segmentation Medical Image Segmentation +2

Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation

no code implementations10 Mar 2023 Zhongying Deng, Da Li, Junjun He, Yi-Zhe Song, Tao Xiang

D-CFA minimizes the domain gap by augmenting the source data with distribution-sampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models.

Unsupervised Domain Adaptation

FCN+: Global Receptive Convolution Makes FCN Great Again

no code implementations8 Mar 2023 Zhongying Deng, Xiaoyu Ren, Jin Ye, Junjun He, Yu Qiao

The motivation of GRC is that different channels of a convolutional filter can have different grid sampling locations across the whole input feature map.

Segmentation Semantic Segmentation

Neural Transformation Fields for Arbitrary-Styled Font Generation

1 code implementation CVPR 2023 Bin Fu, Junjun He, Jianjun Wang, Yu Qiao

Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values.

Disentanglement Font Generation

An evaluation of U-Net in Renal Structure Segmentation

no code implementations6 Sep 2022 Haoyu Wang, Ziyan Huang, Jin Ye, Can Tu, Yuncheng Yang, Shiyi Du, Zhongying Deng, Chenglong Ma, Jingqi Niu, Junjun He

Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications.

Image Segmentation Medical Image Segmentation +2

StructToken : Rethinking Semantic Segmentation with Structural Prior

no code implementations23 Mar 2022 Fangjian Lin, Zhanhao Liang, Sitong Wu, Junjun He, Kai Chen, Shengwei Tian

In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i. e.,} classify each pixel representation to a specific category.

Decision Making Segmentation +1

Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

1 code implementation10 Mar 2022 Lei Zhou, Huidong Liu, Joseph Bae, Junjun He, Dimitris Samaras, Prateek Prasanna

Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis.

Brain Tumor Segmentation Image Classification +4

Dynamic Instance Domain Adaptation

1 code implementation9 Mar 2022 Zhongying Deng, Kaiyang Zhou, Da Li, Junjun He, Yi-Zhe Song, Tao Xiang

In this paper, we address both single-source and multi-source UDA from a completely different perspective, which is to view each instance as a fine domain.

Unsupervised Domain Adaptation

Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition

1 code implementation ECCV 2020 Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, Yu Qiao

To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image.

Multi-Label Classification

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

1 code implementation8 Oct 2020 Jiancheng Yang, Jiajun Chen, Kaiming Kuang, Tiancheng Lin, Junjun He, Bingbing Ni

Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy.

 Ranked #1 on Text-To-Speech Synthesis on 20000 utterances (using extra training data)

Text-To-Speech Synthesis Time Series +2

EfficientFCN: Holistically-guided Decoding for Semantic Segmentation

no code implementations ECCV 2020 Jianbo Liu, Junjun He, Jiawei Zhang, Jimmy S. Ren, Hongsheng Li

State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance.

Segmentation Semantic Segmentation

Tensor Low-Rank Reconstruction for Semantic Segmentation

no code implementations ECCV 2020 Wanli Chen, Xinge Zhu, Ruoqi Sun, Junjun He, Ruiyu Li, Xiaoyong Shen, Bei Yu

Then we use these rank-1 tensors to recover the high-rank context features through our proposed tensor reconstruction module (TRM).

Semantic Segmentation

Dynamic Multi-Scale Filters for Semantic Segmentation

2 code implementations ICCV 2019 Junjun He, Zhongying Deng, Yu Qiao

DMNet is composed of multiple Dynamic Convolutional Modules (DCMs) arranged in parallel, each of which exploits context-aware filters to estimate semantic representation for a specific scale.

Scene Parsing Segmentation +2

Prostate Segmentation using 2D Bridged U-net

no code implementations12 Jul 2018 Wanli Chen, Yue Zhang, Junjun He, Yu Qiao, Yi-fan Chen, Hongjian Shi, Xiaoying Tang

To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size.

Image Segmentation Medical Image Segmentation +2

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