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Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma

2020-05-08 · Kaixin Xu, Ziyuan Zhao, Jiapan Gu, Zeng Zeng, Chan Wan Ying, Lim Kheng Choon, Thng Choon Hua, Pierce KH Chow

Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.

📄 PDF Abstract BibTeX arXiv:2005.04073

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DiagnosticMulti-Label Learning

Inst4DGS: Instance-Decomposed 4D Gaussian Splatting with Multi-Video Label Permutation Learning

2026-03-19 · Yonghan Lee, Dinesh Manocha arxiv

We present Inst4DGS, an instance-decomposed 4D Gaussian Splatting (4DGS) approach with long-horizon per-Gaussian trajectories. While dynamic 4DGS has advanced rapidly, instance-decomposed 4DGS remains underexplored, larg…

On Robust Learning from Noisy Labels: A Permutation Layer Approach

2022-11-29 · Salman AlSubaihi, Mohammed Alkhrashi, Raied Aljadaany, Fahad Albalawi 외

The existence of label noise imposes significant challenges (e.g., poor generalization) on the training process of deep neural networks (DNN). As a remedy, this paper introduces a permutation layer learning approach term…

Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO

2026-03-22 · Jinquan Zheng, Jia Yuan, Jiacheng Yao, Chenyang Gu 외 arxiv

Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors like option positions and label symbols. Existing inference-time debiasing is c…

Attention-based Deep Multiple Instance Learning

2018-02-13 · ICML 2018 7 · Maximilian Ilse, Jakub M. Tomczak, Max Welling

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the b…

Aerial Scene Classification Multiple Instance Learning

Few-shot Learning for Unsupervised Feature Selection

2021-07-02 · Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara

We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually require many instances for feature selection. …

Decoder feature selection Few-Shot Learning