2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)最新文献_第4页

Special Session 5: Processing and Protection of Encrypted Multimedia Data 专题会议5:加密多媒体数据的处理和保护

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2022-04-19 DOI: 10.1109/ipta54936.2022.9784117

引用次数: 0

Ψ-NET: A Novel Encoder-Decoder Architecture for Animal Segmentation Ψ-NET:一种用于动物分割的新型编码器-解码器架构

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2022-04-19 DOI: 10.1109/IPTA54936.2022.9784135

David Norman Díaz Estrada, Utkarsh Goyal, M. Ullah, F. A. Cheikh

引用次数: 0

Monoplanar CT Reconstruction with GANs gan的单平面CT重建

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2022-04-19 DOI: 10.1109/IPTA54936.2022.9784126

Justus Schock, Yu-Chia Lan, D. Truhn, M. Kopaczka, Stefan Conrad, S. Nebelung, D. Merhof

引用次数: 1

Weak supervision using cell tracking annotation and image registration improves cell segmentation 使用细胞跟踪注释和图像配准的弱监督改进了细胞分割

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2022-04-19 DOI: 10.1109/IPTA54936.2022.9784140

N. A. Anoshina, D. Sorokin

引用次数: 0

Pyramid Tokens-to-Token Vision Transformer for Thyroid Pathology Image Classification 用于甲状腺病理图像分类的金字塔标记到标记视觉转换器

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2022-04-19 DOI: 10.1109/IPTA54936.2022.9784139

Peng Yin, Bo Yu, Cheng-wei Jiang, Hechang Chen

{"title":"Pyramid Tokens-to-Token Vision Transformer for Thyroid Pathology Image Classification","authors":"Peng Yin, Bo Yu, Cheng-wei Jiang, Hechang Chen","doi":"10.1109/IPTA54936.2022.9784139","DOIUrl":"https://doi.org/10.1109/IPTA54936.2022.9784139","url":null,"abstract":"Histopathological image contains rich phenotypic information, which is beneficial to classifying tumor subtypes and predicting the development of diseases. The vast size of pathological slides makes it impossible to directly train whole slide images (WSI) on convolutional neural networks (CNNs). Most of the previous weakly supervision works divide high-resolution WSIs into small image patches and separately input them into the CNN to classify them as tumors or normal areas. The first difficulty is that although the method based on the CNN framework achieves a high accuracy rate, it increases the model parameters and computational complexity. The second difficulty is balancing the relationship between accuracy and model compu-tation. It makes the model maintain and improve the classification accuracy as much as possible based on the lightweight. In this paper, we propose a new lightweight architecture called Pyramid Tokens-to-Token VIsion Transformer (PyT2T-ViT) with multiple instance learning based on Vision Transformer. We introduce the feature extractor of the model with Token-to-Token ViT (T2T-ViT) to reduce the model parameters. The performance of the model is improved by combining the image pyramid of multiple receptive fields so that it can take into account the local and global features of the cell structure at a single scale. We applied the method to our collection of 560 thyroid pathology images from the same institution, model parameters and computation were greatly reduced. The classification effect is significantly better than the CNN-based method.","PeriodicalId":381729,"journal":{"name":"2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115703585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning 基于全局和局部感知深度特征表示学习的手部人物识别

2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA) Pub Date : 2021-01-13 DOI: 10.1109/IPTA54936.2022.9784133

Nathanael L. Baisa, Zheheng Jiang, Ritesh Vyas, Bryan Williams, Hossein Rahmani, P. Angelov, Sue Black

{"title":"Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning","authors":"Nathanael L. Baisa, Zheheng Jiang, Ritesh Vyas, Bryan Williams, Hossein Rahmani, P. Angelov, Sue Black","doi":"10.1109/IPTA54936.2022.9784133","DOIUrl":"https://doi.org/10.1109/IPTA54936.2022.9784133","url":null,"abstract":"In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it is important to extend to consider local information. In this work, we propose hand-based person identification by learning both global and local deep feature representations. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features. For learning the local (part-level) features, we perform uniform partitioning on the conv-layer in both horizontal and vertical directions. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches.","PeriodicalId":381729,"journal":{"name":"2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134023170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6