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Showing 1 to 19 of 19 for “"multiple-instance learning"”.
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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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Image database retrieval with multiple-instance learning techniques
… to train the system. During the training, a multiple-instance learning method known as the Diverse Density algorithm is employed to determine which feature vector in each image best represents the user's concept, and which dimensions of the feature vectors are important. The system tries to …
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Implementation of Multiple-Instance Learning in Drug Activity Prediction
… conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule …
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Interpretable Tumor Localization in Bladder Cancer Histopathology Using Deep Multiple Instance Learning
Deep learning has emerged in cancer histopathology as a tool for predicting clinical and molecular properties of a patient’s disease, thereby connecting slide with function. This concept is especially relevant to bladder cancer, where molecular and histopathologic heterogeneity is known to impact …
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Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
… histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge …
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Ontology-based image categorization
… weak attributes are automatically learned by multiple-instance learning to capture visual similarities in a hierarchical way; i.e., different local features are learned to classify objects at different semantic levels. Overall, our approach imitates the human visual system and is more advanced …
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Discovery of Novel Glycogen Synthase Kinase-3beta Inhibitors: Molecular Modeling, Virtual Screening, and Biological Evaluation
… To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated hierarchical QSAR, which adopts a multi-level structure to take data heterogeneity into account. A collection of 728 GSK-3 inhibitors with diverse structural scaffolds were obtained from …
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Computational Models for the Automatic Learning and Recognition of Irish Sign Language
… This is achieved through our proposed Multiple Instance Learning Density Matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilised to train …
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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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Scaffold Perception, ComPharmacophore Model Development, And Quantitative Structure-Affinity Relationships Of Sigma Site Ligands
… clustered by measures of scaffold dissimilarity. Multiple-Instance Learning techniques were used to train classification models that differentiated molecules as active or inactive, and to assist in the identification of relevant conformations of σ ligands at their macromolecular targets. …
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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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Weakly supervised learning from referring expression: Challenge and directions
We explore methods of weakly supervised learning from referring expression. Unlike traditional fully supervised semantic segmentation of object recognition tasks, in which a a small set of discrete class bases is provided, the referring expression task is performed associated with a sentence …
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Communication at the Cellular Level: Discovery and Application
… resolved transcriptomics technologies and multiple-instance learning to enable high-throughput detection of intercellular communications. This new approach addresses a number of shortcomings of existing methods such as poor specificity, limited to certain types of interactions, reliance on …
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scPhen: Single-Cell Phenotype Predictor for Alzheimer’s Disease
… (AI) and generative AI for representation learning have transformed our ability to model complex biological systems. Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution into cellular heterogeneity, offering a powerful substrate for modeling disease circuitry. However, …
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Deep heterogeneous superpixel neural networks for image analysis and feature extraction
… join graphical neural network techniques and multiple-instance learning to achieve weakly supervised object detection and generate precise object bounding without pixel-level training labels. This dissection and the subsequent learning by the architecture promotes explainable models, whereby …
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Robust Deep Learning Methodologies for Weakly Supervised Remote Sensing Image Classification
… studies and environmental monitoring. Deep learning (DL) has proven very effective in addressing the analytical challenges posed by this data, excelling in image analysis and sequential data processing. However, in remote sensing (RS), DL is often hindered by scarce and imperfect labeled …
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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 …
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Recognising and localising human actions
… context and movements which are shared amongst multiple action classes. For example, a waving action may be performed whilst walking, however if the walking movement appears in distinct action classes, then it should not be included in training a waving movement classier. For this reason, we …
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Reframing Cox Proportional Hazards Model for Big Data and Neural Networks
… is the absence of annotation at the patch (instance) level because of the high cost and the time-consuming nature of hand labeling. This challenge is typically mitigated by pooling instances that rely on only the slide-level labels. For example, we see this with typical weakly supervised …