Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation最新文献

A Study of Neuroimaging and Genetic Non-linear Associations For Brain Development. 脑发育的神经影像学和遗传非线性关联研究。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2026-03-01 Epub Date: 2026-08-28 DOI: 10.1109/ssiai68148.2026.11667305

Bhaskar Ray, Dawn Jensen, Ram Sapkota, Jiayu Chen, Vince D Calhoun, Jingyu Liu

{"title":"A Study of Neuroimaging and Genetic Non-linear Associations For Brain Development.","authors":"Bhaskar Ray, Dawn Jensen, Ram Sapkota, Jiayu Chen, Vince D Calhoun, Jingyu Liu","doi":"10.1109/ssiai68148.2026.11667305","DOIUrl":"10.1109/ssiai68148.2026.11667305","url":null,"abstract":"During the transition from childhood to adulthood, the human brain undergoes significant developmental changes that facilitate cognitive maturation, driven by a complex interplay of environmental and genetic factors. Despite abundant studies on neuroimaging for brain development, the specific genetic influences remain unclear. Using brain age estimation, we categorized individuals (N = 7,435, aged 9-10 years) from the Adolescent Brain and Cognitive Development (ABCD) cohort into groups exhibiting either accelerated or delayed brain maturation. This study examines brain imaging and genetic associations within this subpopulation to identify the genetic influences on brain development. We have compared linear association using Sparse Canonical Correlation Analysis (sCCA)-base model and non-linear association with Supervised Contrastive Joint Latent Representation Network (SC-JLRN). Developmentally relevant nonlinear latent patterns between genetic variants highly expressed in the cerebellum and brain regions significantly associated with brain age (cerebellar-hippocamp-frontal circuitry) were extracted.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2026 ","pages":"113-116"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148898545","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}

引用次数: 0

Multimodal Fusion of Structural and Diffusion MRI for Intelligence Prediction. 用于智能预测的结构与扩散MRI多模态融合。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2026-01-01 Epub Date: 2026-06-11 DOI: 10.1109/ssiai68148.2026.11553807

Ram Sapkota, Bishal Thapaliya, Jingyu Liu

{"title":"Multimodal Fusion of Structural and Diffusion MRI for Intelligence Prediction.","authors":"Ram Sapkota, Bishal Thapaliya, Jingyu Liu","doi":"10.1109/ssiai68148.2026.11553807","DOIUrl":"10.1109/ssiai68148.2026.11553807","url":null,"abstract":"Multimodal neuroimaging fusion provides complementary insights into brain structure and/or function. However, effectively integrating features across modalities remains a challenging task. This study presents a deep learning-based multimodal fusion framework for predicting cognitive outcomes in children using data from the Adolescent Brain Cognitive Development (ABCD) Study. We focus on two imaging modalities: gray matter (GM) density derived from structural MRI and white matter fractional anisotropy (FA) derived from diffusion MRI. Modality-specific features were extracted using two separate convolutional neural networks (CNNs) and subsequently integrated through three fusion strategies: simple concatenation, multi-head attention, and transformer encoder-based fusion. We evaluated both single-modality and multimodal models to assess the added value of integration. Experimental results demonstrate that direct feature concatenation achieves the highest predictive performance, surpassing attention-based and transformer-based fusion approaches, with a test correlation of 0.44. Furthermore, we employed guided Grad-CAM to localize GM and FA regions contributing to intelligence prediction, providing interpretable neurological insights into the model's decision-making process.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2026 ","pages":"21-24"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13262964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254835","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}

引用次数: 0

COVID-19 PNEUMONIA CHEST X-RAY PATTERN SYNTHESIS BY STABLE DIFFUSION. 稳定扩散法合成COVID-19肺炎胸片型。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2024-03-01 Epub Date: 2024-04-29 DOI: 10.1109/ssiai59505.2024.10508671

Zhaohui Liang, Zhiyun Xue, Sivaramakrishnan Rajaraman, Sameer Antani

{"title":"COVID-19 PNEUMONIA CHEST X-RAY PATTERN SYNTHESIS BY STABLE DIFFUSION.","authors":"Zhaohui Liang, Zhiyun Xue, Sivaramakrishnan Rajaraman, Sameer Antani","doi":"10.1109/ssiai59505.2024.10508671","DOIUrl":"10.1109/ssiai59505.2024.10508671","url":null,"abstract":"In this study, we fine-tuned a stable diffusion model to synthesize high resolution chest X-ray images (512×512) with bilateral lung edema caused by COVID-19 pneumonia using the class-specific prior preservation strategy. 300 positive images were selected from the MIDRC dataset as subject instances with an additional 400 negative images for class prior preservation. We synthesized images respectively using the new technique and the conventional technique for comparison. The synthetic images by the stable diffusion fine-tuned by the prior preservation technique have the Frechet inception distance (FID) of 9.2158 and kernel inception distance (KID) 0.0818 computed with the real positive images, which is superior to the synthetic images using the conventional methods such as WGAN and DDIM. The classification accuracy is 0.9975 with precision of 1.0 and recall of 0.9950 when the synthetic positive images with the real negative images were classified by a trained vision transformer (ViT). We conclude that the stable diffusion model can synthesize high-quality and high-resolution chest x-ray images using the prior preservation strategy with a small number of real images as subject instances and text prompt as guidance for the designated patterns.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2024 ","pages":"21-24"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000072","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}

引用次数: 0

Spatio-functional parcellation of resting state fMRI. 静息状态fMRI的空间功能分割。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2024-03-01 Epub Date: 2024-04-29 DOI: 10.1109/ssiai59505.2024.10508652

Harshit Parmar, Brian Nutter, Sunanda Mitra, Rodney Long, Sameer Antani

引用次数: 0

AUTOMATED DETECTION OF MALARIAL RETINOPATHY USING TRANSFER LEARNING. 利用迁移学习自动检测疟原虫视网膜病变。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2020-03-01 Epub Date: 2020-05-18 DOI: 10.1109/ssiai49293.2020.9094595

A Kurup, P Soliz, S Nemeth, V Joshi

{"title":"AUTOMATED DETECTION OF MALARIAL RETINOPATHY USING TRANSFER LEARNING.","authors":"A Kurup, P Soliz, S Nemeth, V Joshi","doi":"10.1109/ssiai49293.2020.9094595","DOIUrl":"10.1109/ssiai49293.2020.9094595","url":null,"abstract":"Cerebral Malaria (CM) is a severe neurological syndrome of malaria mainly found in children and is associated with highly specific retinal lesions. The manifestation of these indications of CM in the retina is called malarial retinopathy (MR). All patients showing clinical signs of CM are commonly diagnosed and treated accordingly; however, 23% of them are misdiagnosed as they suffer from another infection with identical clinical symptoms. Due to these underlying symptoms, the false positive cases may go untreated and could result in death of the patients. A diagnostic test is needed that is highly specific in order to reduce false positives. The purpose of this study to demonstrate a technique based on a transfer learning technique using images from three different retinal cameras to identify the hemorrhages and whitening lesions in the retina which can accurately identify the patients with MR. The MR detection model gives a specificity of 100% and a sensitivity of 90% with an AUC of 0.98. The algorithm demonstrates the potential of accurate MR detection with a low-cost retinal camera.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2020 ","pages":"18-21"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591150/pdf/nihms-1636320.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38541238","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}

引用次数: 0

A Maximum-Likelihood Approach for ADC Estimation of Lesions in Visceral Organs. 内脏器官病变 ADC 估算的最大似然法

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2012-01-01 DOI: 10.1109/SSIAI.2012.6202443

Abhinav K Jha, Jeffrey J Rodríguez

{"title":"A Maximum-Likelihood Approach for ADC Estimation of Lesions in Visceral Organs.","authors":"Abhinav K Jha, Jeffrey J Rodríguez","doi":"10.1109/SSIAI.2012.6202443","DOIUrl":"10.1109/SSIAI.2012.6202443","url":null,"abstract":"Accurate estimation of the apparent diffusion coefficient (ADC) of lesions in diffusion-weighted magnetic resonance imaging (DWMRI) is important to predict and monitor anti-cancer therapy response. The task of ADC estimation of lesions is complicated due to noise in the image, different variances in signal strengths at different b values and other random phenomena. In organs that have visceral motion, due to motion across scans, estimating the ADC becomes even more complex. To get rid of inaccuracies due to motion, only a single ADC value of the lesion is estimated, conventionally using a linear-regression (LR) approach. The LR approach is based on an inaccurate noise model and also suffers from other deficiencies. In this paper, we propose an easy-to-implement and computationally-fast maximum-likelihood (ML) method to estimate the ADC value of heterogeneous lesions in visceral organs. The proposed method takes into account the Rician distribution of noise in DWMRI. In the process, we also derive the statistical model for the measured mean signal intensity in DWMRI. We show using Monte-Carlo simulations that that the proposed method is more accurate than the LR method.","PeriodicalId":89229,"journal":{"name":"Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"2012 ","pages":"21-24"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4166577/pdf/nihms394556.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32685578","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}

引用次数: 0

A Clustering Algorithm for Liver Lesion Segmentation of Diffusion-Weighted MR Images. 弥散加权MR图像肝脏病灶分割的聚类算法。

Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation Pub Date : 2010-05-23 DOI: 10.1109/SSIAI.2010.5483911

Abhinav K Jha, Jeffrey J Rodríguez, Renu M Stephen, Alison T Stopeck

引用次数: 0