IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems最新文献

{"title":"Weakly Supervised Learning of Oral Cavity Images for Separating Precancer from Cancer.","authors":"Zhiyun Xue, Niccolo Marini, Sivaramakrishnan Rajaraman, Zhaohui Liang, Sameer Antani","doi":"10.1109/coins69995.2026.11715634","DOIUrl":"https://doi.org/10.1109/coins69995.2026.11715634","url":null,"abstract":"<p><p>Early detection is crucial for reducing the morbidity and mortality associated with oral cancer. Visual examination of the oral cavity is the first step in screening for lesions that are cancer or precancer (i.e., the direct precursor to cancer). In contrast to existing work that uses image or lesion labels, we explore a weakly supervised method to address the situation where only patient-level labels are available. Specifically, we use multiple-instance learning (MIL) to classify patients with oral precancer versus cancer. Our approach involves three steps: image filtering, mouth detection, and patient-level classification. The method performs well on filtering and mouth detection and shows promising results in distinguishing precancer from cancer on an independent test set, demonstrating its potential as a proof-of-concept (PoC). We also discuss current limitations and future directions.</p>","PeriodicalId":521045,"journal":{"name":"IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13664155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198387","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":"Improving Clinical Reliability of Diffusion-Based Dermatology Image Synthesis Through Synthetic Note Alignment and Post-Generation Filtering.","authors":"Niccolo Marini, Zhaohui Liang, Sivaramakrishnan Rajaraman, Zhiyun Xue, Sameer Antani","doi":"10.1109/coins69995.2026.11715620","DOIUrl":"10.1109/coins69995.2026.11715620","url":null,"abstract":"<p><p>The development of dermatology image synthesis methods is gaining increasing attention as a solution to address persistent data scarcity and class imbalance, which constrain the development of reliable foundation models for clinical decision support. Although recent dermatology foundation models achieve dermatologist-level performance, their effectiveness critically depends on large-scale multimodal datasets. Publicly available dermatology datasets are usually limited in size, imbalanced, and often lack structured textual descriptions of lesions, limiting robust training and generalization. In addition, diffusion-based generative models require image-text pairs and may produce hallucinated or diagnostically inconsistent outputs, limiting their safe integration into clinical pipelines. This paper proposes a framework to improve dermatology image synthesis, leveraging synthetic clinical notes as structured conditioning prompts within a Stable Diffusion model, combined with a dedicated semantic filtering strategy to enhance reliability. First, multiple Large Language Models (LLMs) generate structured clinical descriptions to pair with real dermatology images. Second, a dermatology-adapted diffusion model is fine-tuned on these pairs to enable controllable text-conditioned image generation and generates 160,000 synthetic images. Third, an auxiliary foundation model evaluates the semantic consistency of synthetic images and their prompts, filtering hallucinated or diagnostically inconsistent samples. The filtered synthetic images are combined with 16,000 real images from six public repositories to train a foundation model. The evaluation involves fifteen datasets (37,000 images) and two downstream tasks (cross-modal retrieval and zero-shot learning), demonstrating improved performance, robustness, and cross-dataset generalization compared to a baseline model trained without synthetic note conditioning.</p>","PeriodicalId":521045,"journal":{"name":"IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems","volume":"2026 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13649339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140328","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":"Detecting Oral Cancer Using Tabular Deep Learning.","authors":"Zhiyun Xue, Zhaohui Liang, Sivaramakrishnan Rajaraman, Niccolo Marini, Sameer Antani","doi":"10.1109/coins65080.2025.11125786","DOIUrl":"10.1109/coins65080.2025.11125786","url":null,"abstract":"<p><p>Oral cancer has one of the lowest five-year survival rates among major cancer types. Therefore, early detection is crucial for histopathological confirmation. State-of-the-art methods reported in the literature largely analyze images only for oral cancer prediction. The use of deep learning networks for related tabular medical data remains unexplored for oral cancer and understudied in general. As part of our multimodal AI/ML approach toward reliable prediction of candidate lesions to biopsy, we describe our work in deep learning approaches on a fielded clinical structured text data in spreadsheet format (tabular data) on a subset comprising 1791 patients drawn from a large ongoing oral cancer study to classify patients with a cancerous lesion from those with a precancerous lesion (i.e., direct precursor to cancer). We compare two tabular deep learning methods and one conventional algorithm for the predictive data analysis. The experimental results on a hold-out test set demonstrate a promising performance for all models (Youden index > 0.6 and AUC > 0.9). In addition, we examine and analyze the interpretability of models. All models indicate that lesion characteristics are crucial predictive features. The insights and results obtained from this work would be valuable to the research community in application of AI/ML to biomedicine.</p>","PeriodicalId":521045,"journal":{"name":"IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems","volume":"2025 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12418227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042994","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}