Search Results - Multiple-Instance Learning Algorithms*

mi-DS: Multiple-Instance Learning Algorithm by Nguyen, Dat T., Nguyen, Cao D., Hargraves, Rosalyn, Kurgan, Lukasz A., Cios, Krzysztof J.

“…Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances…”
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A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations by Guo, Bangwei, Li, Xingyu, Yang, Miaomiao, Zhang, Hong, Xu, Xu Steven

“… This paper presents a novel, lightweight Attention-based Multiple Instance Mutation Learning (AMIML…”
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Robust objectness tracking with weighted multiple instance learning algorithm by Yang, Honghong, Qu, Shiru, Zhu, Fumin, Zheng, Zunxin

“…A novel improved online weighted multiple instance learning algorithm(IWMIL) for visual tracking is proposed…”
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Multiple-Instance Learning Algorithms for Computer-Aided Detection by Dundar, M. Murat, Fung, Glenn, Krishnapuram, Balaji, Rao, R. Bharat

“… Existing MIL algorithms are much too computationally expensive for these datasets. We describe CH, a framework for learning a convex hull representation of multiple instances that is significantly faster than existing MIL algorithms…”
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Fast Bundle Algorithm for Multiple-Instance Learning by Bergeron, C., Moore, G., Zaretzki, J., Breneman, C. M., Bennett, K. P.

“…We present a bundle algorithm for multiple-instance classification and ranking. These frameworks yield improved models on many problems possessing special structure…”
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Patch Based Multiple Instance Learning Algorithm for Object Tracking by Wang, Zhenjie, Zhang, Hua, Wang, Li Jia

“…To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL…”
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Predicting Response to Neoadjuvant Therapy in Breast Cancer Using Deep Learning on Primary Core Needle Biopsy Slides by LUO Yunzhao, JIANG Hongchuan, XU Feng

“… Objective A deep learning model based on core-needle biopsy whole slide images (WSI) of breast cancer (DL-CNB) was trained using the multiple instance learning…”
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A Generalized Multiple Instance Learning Algorithm with Multiple Selection Strategies for Cross Granular Learning by Kang, F., Naphade, M. R.

“… This can speed up annotation significantly. Using the multiple instance learning paradigm, we show that it is possible to learn representations of concepts occurring at the regional level by using annotations for several images…”
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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

“… To provide an economical and rapid way to predict patients' TMB, the KMeansGraphMIL model was proposed based on weakly supervised multiple-instance learning…”
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Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm by Wang, Li Jia, Zhang, Hua

“…An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed…”
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The Kernel Based Multiple Instances Learning Algorithm for Object Tracking by Han, Tiwen, Wang, Lijia, Wen, Binbin

“… Then, the learning rate will be set to be a…”
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Predicting Nottingham grade in breast cancer digital pathology using a foundation model by Kim, Jun Seo, Lee, Jeong Hoon, Yeon, Yousung, An, Doyeon, Kim, Seok Jun, Noh, Myung-Giun, Lee, Suehyun

“…Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide…”
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Beyond accuracy: Quantifying the reliability of multiple instance learning for whole slide image classification by Keshvarikhojasteh, Hassan, Aubreville, Marc, Bertram, Christof A, Pluim, Josien P W, Veta, Mitko

“… Multiple Instance Learning (MIL) models designed for Whole Slide Image (WSI) classification in computational pathology are rarely evaluated in terms of reliability…”
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Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach by Bobowicz, Maciej, Rygusik, Marlena, Buler, Jakub, Buler, Rafał, Ferlin, Maria, Kwasigroch, Arkadiusz, Szurowska, Edyta, Grochowski, Michał

“… We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM…”
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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

“… Multiple instance learning (MIL) approaches, including a transformer variant, were tested to predict lesions within a weakly supervised framework…”
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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

“… Multiple instance learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution…”
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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…”
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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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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

“… Here, we describe an interpretable machine learning approach, additive multiple instance learning (aMIL…”
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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

“… Multi-instance learning (MIL) has become the mainstream method for analyzing whole-slide images (WSIs…”
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