Search Results for author: Xiaofan Zhang

Found 37 papers, 19 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.

Transfer Learning

USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis

no code implementations30 Dec 2023 Jing Jiao, Jin Zhou, Xiaokang Li, Menghua Xia, Yi Huang, Lihong Huang, Na Wang, Xiaofan Zhang, Shichong Zhou, Yuanyuan Wang, Yi Guo

In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis.

Image Enhancement

PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stains

1 code implementation15 Dec 2023 Shengyi Hua, Fang Yan, Tianle Shen, Xiaofan Zhang

In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology.

Self-Supervised Learning

ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

no code implementations7 Dec 2023 Yankai Jiang, Zhongzhen Huang, Rongzhao Zhang, Xiaofan Zhang, Shaoting Zhang

In this paper, we propose a new zero-shot pan-tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set.

Organ Segmentation Segmentation +1

Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation

no code implementations24 Jul 2023 Qi Su, Na Wang, Jiawen Xie, Yinan Chen, Xiaofan Zhang

Therefore, we propose a new automatic lung lobe segmentation framework, in which we urge the model to pay attention to the area around the pulmonary fissure during the training process, which is realized by a task-specific loss function.

Segmentation

Efficient Subclass Segmentation in Medical Images

1 code implementation1 Jul 2023 Linrui Dai, Wenhui Lei, Xiaofan Zhang

One feasible way to reduce the cost is to annotate with coarse-grained superclass labels while using limited fine-grained annotations as a complement.

Segmentation Semantic Segmentation

MedLSAM: Localize and Segment Anything Model for 3D CT Images

1 code implementation26 Jun 2023 Wenhui Lei, Xu Wei, Xiaofan Zhang, Kang Li, Shaoting Zhang

Our findings are twofold: 1) MedLAM is capable of directly localizing any anatomical structure using just a few template scans, yet its performance surpasses that of fully supervised models; 2) MedLSAM not only aligns closely with the performance of SAM and its specialized medical adaptations with manual prompts but achieves this with minimal reliance on extreme point annotations across the entire dataset.

Image Segmentation Semantic Segmentation

KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

no code implementations CVPR 2023 Zhongzhen Huang, Xiaofan Zhang, Shaoting Zhang

Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing.

Clinical Knowledge

Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization

no code implementations8 Jun 2023 Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang, Chiachen Chou, Tom Jablin, Jian Li, Elfie Guo, Caitlin Stanton, Siddharth Joshi, Yu Emma Wang

To address this challenge, we propose a mixed-precision post training quantization (PTQ) approach that assigns different numerical precisions to tensors in a network based on their specific needs, for a reduced memory footprint and improved latency while preserving model accuracy.

Quantization

MidMed: Towards Mixed-Type Dialogues for Medical Consultation

1 code implementation5 Jun 2023 Xiaoming Shi, Zeming Liu, Chuan Wang, Haitao Leng, Kui Xue, Xiaofan Zhang, Shaoting Zhang

To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat.

Dialogue Generation

Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search

no code implementations2 Feb 2023 Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang

In this paper, we propose a method to efficiently determine quantization configurations of different tensors in ML models using post-training mixed precision quantization.

Quantization

Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures

1 code implementation18 Aug 2022 Ran Gu, Jingyang Zhang, Guotai Wang, Wenhui Lei, Tao Song, Xiaofan Zhang, Kang Li, Shaoting Zhang

To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain.

Anatomy Contrastive Learning +4

Compilation and Optimizations for Efficient Machine Learning on Embedded Systems

no code implementations6 Jun 2022 Xiaofan Zhang, Yao Chen, Cong Hao, Sitao Huang, Yuhong Li, Deming Chen

Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc.

BIG-bench Machine Learning

Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation

1 code implementation13 May 2022 Ran Gu, Jiangshan Lu, Jingyang Zhang, Wenhui Lei, Xiaofan Zhang, Guotai Wang, Shaoting Zhang

To tackle this deficiency, we propose Contrastive Domain Disentangle (CDD) network for generalizable medical image segmentation.

Disentanglement Domain Generalization +4

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models

no code implementations21 Jan 2022 Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang

By evaluating on SQuAD, a model found by AutoDistill achieves an 88. 4% F1 score with 22. 8M parameters, which reduces parameters by more than 62% while maintaining higher accuracy than DistillBERT, TinyBERT, and NAS-BERT.

Bayesian Optimization Knowledge Distillation +2

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search

1 code implementation24 Nov 2021 Qian Jiang, Xiaofan Zhang, Deming Chen, Minh N. Do, Raymond A. Yeh

In this work, we propose End-to-end Hardware-aware DNAS (EH-DNAS), a seamless integration of end-to-end hardware benchmarking, and fully automated DNAS to deliver hardware-efficient deep neural networks on various platforms, including Edge GPUs, Edge TPUs, Mobile CPUs, and customized accelerators.

Benchmarking Neural Architecture Search

One-shot Weakly-Supervised Segmentation in Medical Images

1 code implementation21 Nov 2021 Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang

Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation.

Denoising Image Segmentation +5

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

4 code implementations3 Nov 2021 Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang

Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.

Image Segmentation Medical Image Segmentation +4

F-CAD: A Framework to Explore Hardware Accelerators for Codec Avatar Decoding

no code implementations8 Mar 2021 Xiaofan Zhang, Dawei Wang, Pierce Chuang, Shugao Ma, Deming Chen, Yuecheng Li

Creating virtual avatars with realistic rendering is one of the most essential and challenging tasks to provide highly immersive virtual reality (VR) experiences.

Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space

1 code implementation1 Jan 2021 Yuhong Li, Cong Hao, Xiaofan Zhang, JinJun Xiong, Wen-mei Hwu, Deming Chen

This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.

Image Classification Neural Architecture Search

Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices

no code implementations14 Oct 2020 Cong Hao, Yao Chen, Xiaofan Zhang, Yuhong Li, JinJun Xiong, Wen-mei Hwu, Deming Chen

High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.

EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions

no code implementations6 May 2020 Yuhong Li, Cong Hao, Xiaofan Zhang, Xinheng Liu, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.

Neural Architecture Search

HybridDNN: A Framework for High-Performance Hybrid DNN Accelerator Design and Implementation

no code implementations8 Apr 2020 Hanchen Ye, Xiaofan Zhang, Zhize Huang, Gengsheng Chen, Deming Chen

To speedup Deep Neural Networks (DNN) accelerator design and enable effective implementation, we propose HybridDNN, a framework for building high-performance hybrid DNN accelerators and delivering FPGA-based hardware implementations.

AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs

1 code implementation6 Jan 2020 Pengfei Xu, Xiaofan Zhang, Cong Hao, Yang Zhao, Yongan Zhang, Yue Wang, Chaojian Li, Zetong Guan, Deming Chen, Yingyan Lin

Specifically, AutoDNNchip consists of two integrated enablers: (1) a Chip Predictor, built on top of a graph-based accelerator representation, which can accurately and efficiently predict a DNN accelerator's energy, throughput, and area based on the DNN model parameters, hardware configuration, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selection, block configuration, resource balancing, etc.

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

1 code implementation25 Jun 2019 Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, JinJun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen

Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.

object-detection Object Detection

A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices

2 code implementations20 May 2019 Xiaofan Zhang, Cong Hao, Yuhong Li, Yao Chen, JinJun Xiong, Wen-mei Hwu, Deming Chen

Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.

object-detection Object Detection

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

2 code implementations9 Apr 2019 Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, JinJun Xiong, Kyle Rupnow, Wen-mei Hwu, Deming Chen

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.

C++ code object-detection +1

Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

1 code implementation CVPR 2019 Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, Heng Huang

In this paper, we propose a novel end-to-end trainable Video Question Answering (VideoQA) framework with three major components: 1) a new heterogeneous memory which can effectively learn global context information from appearance and motion features; 2) a redesigned question memory which helps understand the complex semantics of question and highlights queried subjects; and 3) a new multimodal fusion layer which performs multi-step reasoning by attending to relevant visual and textual hints with self-updated attention.

Question Answering Video Question Answering +1

SiamVGG: Visual Tracking using Deeper Siamese Networks

4 code implementations7 Feb 2019 Yuhong Li, Xiaofan Zhang, Deming Chen

It combines a Convolutional Neural Network (CNN) backbone and a cross-correlation operator, and takes advantage of the features from exemplary images for more accurate object tracking.

Visual Object Tracking Visual Tracking

Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA

no code implementations31 Jul 2018 Junsong Wang, Qiuwen Lou, Xiaofan Zhang, Chao Zhu, Yonghua Lin, Deming Chen

To create such accelerators, we propose a design flow for accelerating the extremely low bit-width neural network (ELB-NN) in embedded FPGAs with hybrid quantization schemes.

Edge-computing Quantization

Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs

no code implementations23 Mar 2018 Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, JinJun Xiong, Deming Chen

Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks.

Face Recognition

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

13 code implementations CVPR 2018 Yuhong Li, Xiaofan Zhang, Deming Chen

We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.

Crowd Counting Scene Recognition

Embedding Label Structures for Fine-Grained Feature Representation

no code implementations CVPR 2016 Xiaofan Zhang, Feng Zhou, Yuanqing Lin, Shaoting Zhang

However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e. g., discovering cars from the same make or the same model, both of which require high precision.

Fine-Grained Image Classification General Classification +3

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