Search Results - "Multiple-Instance Learning Algorithms"

The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden by Chen, Linghao, Xiao, Huiling, Jiang, Jiale, Li, Bing, Liu, Weixiang, Huang, Wensheng

“…Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed…”
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CAMIL: channel attention-based multiple instance learning for whole slide image classification by Mao, Jinyang, Xu, Junlin, Tang, Xianfang, Liu, Yongjin, Zhao, Heaven, Tian, Geng, Yang, Jialiang

“…Motivation The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple instance learning (MIL)…”
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Disentangled Pseudo-Bag Augmentation for Whole Slide Image Multiple Instance Learning by Dong, Jiuyang, Jiang, Junjun, Jiang, Kui, Li, Jiahan, Cai, Linghan, Zhang, Yongbing

“…As the predominant approach for pathological whole slide image (WSI) classification, multiple instance learning (MIL) methods struggle with limited labeled…”
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Comparison of an Attention-Based Multiple Instance Learning (MIL) With a Visual Transformer Model: Two Weakly Supervised Deep Learning (DL) Algorithms for the Detection of Histopathologic Lesions in the Rat Liver to Distinguish Normal From Abnormal by Funk, Juergen, Clement, Gregoire, Togninalli, Matteo, Cohen, Yaniv, Albrecht, Tom, Sullivan, Ruth, Pous, Josep Arus, Tecilla, Marco, Romero-Palomo, Fernando, Abramowski, Amadeusz, D'Annunzio, Angelo, Li, Yun Yvonna, Nguyen, Trung, Hu, Fangyao, Schumacher, Vanessa

“…The histopathologic evaluation of regulatory toxicity studies using artificial intelligence (AI) has the potential to increase study efficiency. For example,…”
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When multiple instance learning meets foundation models: Advancing histological whole slide image analysis by Xu, Hongming, Wang, Mingkang, Shi, Duanbo, Qin, Huamin, Zhang, Yunpeng, Liu, Zaiyi, Madabhushi, Anant, Gao, Peng, Cong, Fengyu, Lu, Cheng

“…Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification…”
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Optimized multiple instance learning for brain tumor classification using weakly supervised contrastive learning by Lu, Kaoyan, Lin, Shiyu, Xue, Kaiwen, Huang, Duoxi, Ji, Yanghong

“…Brain tumors have a great impact on patients’ quality of life and accurate histopathological classification of brain tumors is crucial for patients’ prognosis…”
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EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning by Jeon, Jaemin, Yu, Suwan, Lee, Sangam, Kim, Sang Cheol, Jo, Hye-Yeong, Jung, Inuk, Kim, Kwangsoo

“…Motivation Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association…”
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Spatial Mapping of Gene Signatures in Hematoxylin and Eosin-Stained Images: A Proof of Concept for Interpretable Predictions Using Additive Multiple Instance Learning by Markey, Miles, Kim, Juhyun, Goldstein, Zvi, Gerardin, Ylaine, Brosnan-Cashman, Jacqueline, Javed, Syed Ashar, Juyal, Dinkar, Pagidela, Harshith, Yu, Limin, Rahsepar, Bahar, Abel, John, Hennek, Stephanie, Khosla, Archit, Taylor-Weiner, Amaro, Parmar, Chintan

“…The relative abundance of cancer-associated fibroblast (CAF) subtypes influences a tumor's response to treatment, especially immunotherapy. However, the gene…”
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FR-MIL: Distribution Re-Calibration-Based Multiple Instance Learning With Transformer for Whole Slide Image Classification by Chikontwe, Philip, Kim, Meejeong, Jeong, Jaehoon, Jung Sung, Hyun, Go, Heounjeong, Jeong Nam, Soo, Park, Sang Hyun

“…In digital pathology, whole slide images (WSI) are crucial for cancer prognostication and treatment planning. WSI classification is generally addressed using…”
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AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes by Cai, Linghan, Huang, Shenjin, Zhang, Ye, Lu, Jinpeng, Zhang, Yongbing

“…Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing…”
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KS-TMIL: A K-Stage Transformer approach with multiple instance learning model for ovarian cancer subtype classification by Sai, Marreddi Jayanth, Punn, Narinder Singh

“…Existing multiple instance learning (MIL) methods treat whole slide image (WSI) as collections of independent patches, neglecting these crucial spatial and…”
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A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation by Ko, Seokhwan, Ando, Yu, Kim, Moonsik, Park, Nora Jee-Young, Han, Hyungsoo, Park, Ji Young, Cho, Junghwan

“…Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive…”
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Incorporating hierarchical information into multiple instance learning for patient phenotype prediction with single-cell RNA-sequencing data by Do, Chau, Lähdesmäki, Harri

“…Multiple instance learning (MIL) provides a structured approach to patient phenotype prediction with single-cell RNA-sequencing (scRNA-seq) data. However,…”
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Weakly Supervised Multiple Instance Learning Model With Generalization Ability for Clinical Adenocarcinoma Screening on Serous Cavity Effusion Pathology by Zhang, Yupeng, Zhu, Xiaolong, Zhong, Li, Wu, Jingjing, Chen, Jianling, Yang, Hongqin, Zhang, Sheng, Wang, Kun, Zeng, Saifan

“…Accurate and rapid screening of adenocarcinoma cells in serous cavity effusion is vital in diagnosing the stage of metastatic tumors and providing prompt…”
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S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning by Oh, Hyun-Jic, Min, Seonghui, Jeong, Won-Ki

“…Deep learning-based pathology nuclei segmentation algorithms have demonstrated remarkable performance. Conventional methods mostly focus on supervised…”
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CoD-MIL: Chain-of-Diagnosis Prompting Multiple Instance Learning for Whole Slide Image Classification by Shi, Jiangbo, Li, Chen, Gong, Tieliang, Wang, Chunbao, Fu, Huazhu

“…Multiple instance learning (MIL) has emerged as a prominent paradigm for processing the whole slide image with pyramid structure and giga-pixel size in digital…”
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DepressionMIGNN: A Multiple-Instance Learning-Based Depression Detection Model with Graph Neural Networks by Zhao, Shiwen, Zhang, Yunze, Su, Yikai, Su, Kaifeng, Liu, Jiemin, Wang, Tao, Yu, Shiqi

“…The global prevalence of depression necessitates the application of technological solutions, particularly sensor-based systems, to augment scarce resources for…”
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Whole slide image-level classification of malignant effusion cytology using clustering-constrained attention multiple instance learning by Kim, Dongwoo, Lee, Jongwon, Jung, Minsoo, Yim, Kwangil, Hwang, Gisu, Yoon, Hongjun, Jeong, Daeky, Cho, Won June, Alam, Mohammad Rizwan, Gong, Gyungyub, Cho, Nam Hoon, Yoo, Chong Woo, Chong, Yosep, Seo, Kyung Jin

“…•Cytological diagnosis of pleural fluid has primarily been explored at the image-patch level using artificial intelligence.•This is the first study to classify…”
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Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and clinical classification by Kurata, Yu, Mimura, Imari, Kodera, Satoshi, Abe, Hiroyuki, Yamada, Daisuke, Kume, Haruki, Ushiku, Tetsuo, Tanaka, Tetsuhiro, Takeda, Norihiko, Nangaku, Masaomi

“…Recently developed pathology foundation models, pretrained on large-scale pathology datasets, have demonstrated excellent performance in various downstream…”
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BPMambaMIL: A bio-inspired prototype-guided multiple instance learning for oncotype DX risk assessment in histopathology by Guo, Yongxin, Su, Ziyu, Koyun, Onur C., Lu, Hao, Wesolowski, Robert, Tozbikian, Gary, Niazi, M. Khalid Khan, Gurcan, Metin N.

“…•BPMambaMIL, a novel prototype-guided model, enhances breast cancer recurrence prediction from pathology images.•Bio-inspired prototype selection effectively…”
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