APSIPA Transactions on Signal and Information Processing最新文献

{"title":"Individualized Treatment Effect Inference of Head and Neck Cancer with Multimodal Data.","authors":"Yawen Wei, Zhen Li, Jonghye Woo, Jinsong Ouyang, Georges El Fakhri, Xiaofeng Liu","doi":"10.1108/atsip-06-2025-0051","DOIUrl":"10.1108/atsip-06-2025-0051","url":null,"abstract":"<p><p>Estimating individualized treatment effects (ITE) is critical for personalized medicine, yet it remains a challenge due to retrospective observational data, which suffer from selection bias in clinical practice, and the complexity of multimodal data used for patient status depiction. In this work, we develop an end-to-end deep learning (DL) framework that incorporates multimodal patient data and multiple treatments for accurate ITE inference in a retrospective head and neck cancer (HNC) study. A possible solution is concatenating the factors and adapt adversarial training, which has shown great promise on tabular data, to disentangle patient characteristics from patient status features to mitigate treatment selection bias. However, this approach suffers from instability when applied to complex multimodal patient data and multiple treatment options. For flexible and efficient, treatment-conditioned information fusion, we propose a bi-stage adaptive instance normalization (Bi-AdaIN) to inject relevant factors into corresponding layers, an approach that is also robust to missing values. Furthermore, we propose to disentangle status features from the multi-treatment variable using mutual information (MI) regularization, enabling more accurate predictions of patient-specific outcomes for both factual and counterfactual data. We evaluated our model on the RADCURE dataset, comprising 3,346 HNC cases with CT scans and multiple clinical variables who received radiotherapy or additional chemotherapy and EGFRI. The Bias-Adjusted Treatment Effect (BATE) is substantially reduced compared to the conventional direct ITE method (which does not consider treatment bias) and to adversarial training, indicating a more robust estimation of causal effects. This work is one of the first DL-based studies to address ITE estimation using multimodal medical imaging, offering a promising approach to counterfactual reasoning in clinical oncology for decision support.</p>","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"15 2","pages":"3-20"},"PeriodicalIF":2.1,"publicationDate":"2026-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13160251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology","authors":"Vasileios Magoulianitis, Catherine A. Alexander, C.-C. Jay Kuo","doi":"10.1561/116.00000157","DOIUrl":"https://doi.org/10.1561/116.00000157","url":null,"abstract":"In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"42 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139350409","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}

{"title":"Noise Variance Estimation Using Asymptotic Residual in Compressed Sensing","authors":"Ryo Hayakawa","doi":"10.1561/116.00000215","DOIUrl":"https://doi.org/10.1561/116.00000215","url":null,"abstract":"In compressed sensing, the measurement is usually contaminated by additive noise, and hence the information of the noise variance is often required to design algorithms. In this paper, we propose an estimation method for the unknown noise variance in compressed sensing problems. The proposed method called asymptotic residual matching (ARM) estimates the noise variance from a single measurement vector on the basis of the asymptotic result for the $ell_{1}$ optimization problem. Specifically, we derive the asymptotic residual corresponding to the $ell_{1}$ optimization and show that it depends on the noise variance. The proposed ARM approach obtains the estimate by comparing the asymptotic residual with the actual one, which can be obtained by the empirical reconstruction without the information of the noise variance. Simulation results show that the proposed noise variance estimation outperforms a conventional method based on the analysis of the ridge regularized least squares. We also show that, by using the proposed method, we can achieve good reconstruction performance in compressed sensing even when the noise variance is unknown.","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135612524","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}

{"title":"Movable Virtual Sound Source Construction Based on Wave Field Synthesis using a Linear Parametric Loudspeaker Array","authors":"Yuting Geng, Shiori Sayama, M. Nakayama, T. Nishiura","doi":"10.1561/116.00000043","DOIUrl":"https://doi.org/10.1561/116.00000043","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"26 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081476","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}

{"title":"EEG-based Auditory Attention Detection in Cocktail Party Environment","authors":"Siqi Cai, Hongxu Zhu, Tanja Schultz, Haizhou Li","doi":"10.1561/116.00000128","DOIUrl":"https://doi.org/10.1561/116.00000128","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081492","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}

{"title":"FOANet: A Feedback Operation-Attention Network for Single Image Haze Removal","authors":"Chia-Lin Liu, Lei Chen, Ling Lo, Pin-Jui Huang, Hong-Han Shuai, Wen-Huang Cheng, Ching-Hsuan Wang, Fan Chou","doi":"10.1561/116.00000144","DOIUrl":"https://doi.org/10.1561/116.00000144","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081605","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}

{"title":"Pulse Position Modulation with Flexible Dimming Support for Visible Light Communication","authors":"Poompat Saengudomlert, Karel L. Sterckx","doi":"10.1561/116.00000117","DOIUrl":"https://doi.org/10.1561/116.00000117","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134980505","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}

{"title":"CNN Pretrained Model with Shape Bias using Image Decomposition","authors":"Akinori Iwata, Masahiro Okuda","doi":"10.1561/116.00000113","DOIUrl":"https://doi.org/10.1561/116.00000113","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134980827","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}

{"title":"Challenges and Opportunities in Medical Artificial Intelligence","authors":"Chieh-Mei Tsai, Chieh-Ju Chao, Yung-Chun Chang, Chung-Chieh Jay Kuo, Albert Hsiao, Alexander Shieh","doi":"10.1561/116.00000152","DOIUrl":"https://doi.org/10.1561/116.00000152","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135594884","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}

{"title":"Repeated Update of Demixing Vectors in Independent Low-rank Matrix Analysis for Better Separation","authors":"Taishi Nakashima, Nobutaka Ono","doi":"10.1561/116.00000080","DOIUrl":"https://doi.org/10.1561/116.00000080","url":null,"abstract":"","PeriodicalId":44812,"journal":{"name":"APSIPA Transactions on Signal and Information Processing","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67081292","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}