no code implementations • 28 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.
1 code implementation • 14 Feb 2024 • Yutao Hu, Tianbin Li, Quanfeng Lu, Wenqi Shao, Junjun He, Yu Qiao, Ping Luo
Importantly, all images in this benchmark are sourced from authentic medical scenarios, ensuring alignment with the requirements of the medical field and suitability for evaluating LVLMs.
1 code implementation • 8 Feb 2024 • Peng Gao, Renrui Zhang, Chris Liu, Longtian Qiu, Siyuan Huang, Weifeng Lin, Shitian Zhao, Shijie Geng, Ziyi Lin, Peng Jin, Kaipeng Zhang, Wenqi Shao, Chao Xu, Conghui He, Junjun He, Hao Shao, Pan Lu, Hongsheng Li, Yu Qiao
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX.
Ranked #5 on Video Question Answering on MVBench
1 code implementation • 15 Dec 2023 • Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
no code implementations • 15 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.
1 code implementation • 13 Dec 2023 • Chenglong Ma, Zilong Li, Junjun He, Junping Zhang, Yi Zhang, Hongming Shan
To enjoy the multi-setting synergy in a single model, we propose a novel Prompted Contextual Transformer (ProCT) for incomplete-view CT reconstruction.
no code implementations • 7 Dec 2023 • Pengcheng Chen, Ziyan Huang, Zhongying Deng, Tianbin Li, Yanzhou Su, Haoyu Wang, Jin Ye, Yu Qiao, Junjun He
OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications.
1 code implementation • 20 Nov 2023 • Jin Ye, Junlong Cheng, Jianpin Chen, Zhongying Deng, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Min Zhu, Shaoting Zhang, Junjun He, Yu Qiao
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes.
1 code implementation • 23 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.
2 code implementations • 7 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.
3 code implementations • 30 Aug 2023 • Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Junjun He, Shaoting Zhang, Min Zhu, Yu Qiao
To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
1 code implementation • 26 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.
1 code implementation • 22 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.
1 code implementation • 16 Jun 2023 • Dequan Wang, Xiaosong Wang, Lilong Wang, Mengzhang Li, Qian Da, Xiaoqiang Liu, Xiangyu Gao, Jun Shen, Junjun He, Tian Shen, Qi Duan, Jie Zhao, Kang Li, Yu Qiao, Shaoting Zhang
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
no code implementations • 13 Apr 2023 • Ziyan Huang, Haoyu Wang, Zhongying Deng, Jin Ye, Yanzhou Su, Hui Sun, Junjun He, Yun Gu, Lixu Gu, Shaoting Zhang, Yu Qiao
However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions.
1 code implementation • 31 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.
1 code implementation • 11 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.
no code implementations • 10 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.
no code implementations • 8 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.
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.
1 code implementation • 14 Oct 2022 • Jin Ye, Haoyu Wang, Ziyan Huang, Zhongying Deng, Yanzhou Su, Can Tu, Qian Wu, Yuncheng Yang, Meng Wei, Jingqi Niu, Junjun He
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
no code implementations • 6 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.
1 code implementation • 17 May 2022 • Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Yu Qiao
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT).
Ranked #4 on Semantic Segmentation on PASCAL Context
no code implementations • 23 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.
1 code implementation • 10 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.
1 code implementation • 9 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.
1 code implementation • 16 Feb 2022 • Aaron Babier, Rafid Mahmood, Binghao Zhang, Victor G. L. Alves, Ana Maria Barragán-Montero, Joel Beaudry, Carlos E. Cardenas, Yankui Chang, Zijie Chen, Jaehee Chun, Kelly Diaz, Harold David Eraso, Erik Faustmann, Sibaji Gaj, Skylar Gay, Mary Gronberg, Bingqi Guo, Junjun He, Gerd Heilemann, Sanchit Hira, Yuliang Huang, Fuxin Ji, Dashan Jiang, Jean Carlo Jimenez Giraldo, Hoyeon Lee, Jun Lian, Shuolin Liu, Keng-Chi Liu, José Marrugo, Kentaro Miki, Kunio Nakamura, Tucker Netherton, Dan Nguyen, Hamidreza Nourzadeh, Alexander F. I. Osman, Zhao Peng, José Darío Quinto Muñoz, Christian Ramsl, Dong Joo Rhee, Juan David Rodriguez, Hongming Shan, Jeffrey V. Siebers, Mumtaz H. Soomro, Kay Sun, Andrés Usuga Hoyos, Carlos Valderrama, Rob Verbeek, Enpei Wang, Siri Willems, Qi Wu, Xuanang Xu, Sen yang, Lulin Yuan, Simeng Zhu, Lukas Zimmermann, Kevin L. Moore, Thomas G. Purdie, Andrea L. McNiven, Timothy C. Y. Chan
The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.
no code implementations • 26 Sep 2021 • Junjun He, Jin Ye, Cheng Li, Diping Song, Wanli Chen, Shanshan Wang, Lixu Gu, Yu Qiao
Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images.
no code implementations • 26 Sep 2021 • Zijie Chen, Cheng Li, Junjun He, Jin Ye, Diping Song, Shanshan Wang, Lixu Gu, Yu Qiao
An essential step of RT planning is the accurate segmentation of various organs-at-risks (OARs) in HaN CT images.
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.
Ranked #22 on Multi-Label Classification on MS-COCO
1 code implementation • 8 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)
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.
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).
no code implementations • ECCV 2020 • Jianbo Liu, Junjun He, Jimmy S. Ren, Yu Qiao, Hongsheng Li
Long-range contextual information is essential for achieving high-performance 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.
1 code implementation • CVPR 2019 • Junjun He, Zhongying Deng, Lei Zhou, Yali Wang, Yu Qiao
Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks.
Ranked #34 on Thermal Image Segmentation on MFN Dataset
no code implementations • 12 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.