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Showing 1 to 4 of 4 for “"multiple instance learning (mil)"”.
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A Probabilistic Approach To Multiple-Instance Learning
… study introduced a probabilistic approach to the multiple-instance learning (mil) problem. In particular, two bayes classication algorithms were proposed where posterior probabilities were estimated under dierent assumptions. The rst algorithm, named instance-vote, assumes that the probability of …
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Towards a Reliable Deep Learning Framework for Prostate Cancer Diagnosis using Ultrasound
… for improving patient outcomes. Developing deep learning (DL) models for PCa detection is hindered by noisy labels and cancer heterogeneity. The purpose of this work is to develop a clinically applicable framework for DL-based detection of PCa from ultrasound that is robust to noise and …
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Unsupervised video segmentation and its application to activity recognition
… used to determine the activity label, we used Multiple Instance Learning (MIL) to formulate the problem. Latent variables included a tube index and the parts location under the root template. Experiments were conducted on three well-known datasets and a state-of-the-art result was achieved.
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Generation and analysis of segmentation trees for natural images
… stereo image pairs. Finally, we propose a novel multiple instance learning (MIL) method. In MIL, in contrast to classical supervised learning, the entities to be classified are called bags, each of which contains an arbitrary number of elements called instances. We propose an additive model for …