Search Results for author: Jichen Yang

Found 11 papers, 3 papers with code

Adaptive-avg-pooling based Attention Vision Transformer for Face Anti-spoofing

no code implementations10 Jan 2024 Jichen Yang, Fangfan Chen, Rohan Kumar Das, Zhengyu Zhu, Shunsi Zhang

In this work, we propose a novel vision transformer referred to as adaptive-avg-pooling based attention vision transformer (AAViT) that uses modules of adaptive average pooling and attention to replace the module of average value computing.

Avg Face Anti-Spoofing

Segment Anything Model for Medical Image Analysis: an Experimental Study

2 code implementations20 Apr 2023 Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, Yixin Zhang

We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.

Image Segmentation Interactive Segmentation +5

Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset

no code implementations27 Jul 2022 Jingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda, Jichen Yang, Lisa M. Ho, Brian C. Allen, Wendy L. Ehieli, Chad M. Miller, Jikai Zhang, Maciej A. Mazurowski

Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.

Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists

no code implementations25 Jul 2022 Albert Swiecicki, Nianyi Li, Jonathan O'Donnell, Nicholas Said, Jichen Yang, Richard C. Mather, William A. Jiranek, Maciej A. Mazurowski

A novel deep learning-based method was utilized for assessment of knee OA in two steps: (1) localization of knee joints in the images, (2) classification according to the KL grading system.

Test

Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance

1 code implementation16 Mar 2022 Hanxue Gu, Keyu Li, Roy J. Colglazier, Jichen Yang, Michael Lebhar, Jonathan O'Donnell, William A. Jiranek, Richard C. Mather, Rob J. French, Nicholas Said, Jikai Zhang, Christine Park, Maciej A. Mazurowski

We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score.

GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery

no code implementations16 Jan 2021 Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, Jordan Malof

Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.

Reaction-subdiffusion systems and memory: spectra, Turing instability and decay estimates

no code implementations9 Oct 2019 Jichen Yang, Jens D. M. Rademacher

The modelling of linear and nonlinear reaction-subdiffusion processes is more subtle than normal diffusion and causes different phenomena.

Analysis of PDEs 34A08, 34D20, 35B35, 35R11

PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks

no code implementations11 Sep 2019 Hang Yu, Aishan Liu, Xianglong Liu, Gengchao Li, Ping Luo, Ran Cheng, Jichen Yang, Chongzhi Zhang

In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods.

Data Augmentation

Generative x-vectors for text-independent speaker verification

no code implementations17 Sep 2018 Longting Xu, Rohan Kumar Das, Emre Yilmaz, Jichen Yang, Haizhou Li

Speaker verification (SV) systems using deep neural network embeddings, so-called the x-vector systems, are becoming popular due to its good performance superior to the i-vector systems.

Test Text-Independent Speaker Verification

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