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Found 13 papers, 3 papers with code
An Aggregation of Aggregation Methods in Computational Pathology
Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels.
Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images
Unique gene expression profiles were also identified with respect to HPV infection status, and is in line with existing findings.
REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology
Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows.
Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits
Computational Pathology (CPath) is an emerging field concerned with the study of tissue pathology via computational algorithms for the processing and analysis of digitized high-resolution images of tissue slides.
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology
Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance.
Learning Neural Activations
An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output.
Generalized Learning with Rejection for Classification and Regression Problems
We have demonstrated the applicability and effectiveness of the method on synthetically generated data as well as benchmark datasets from UCI machine learning repository for both classification and regression problems.
An embarrassingly simple approach to neural multiple instance classification
Multiple Instance Learning (MIL) is a weak supervision learning paradigm that allows modeling of machine learning problems in which labels are available only for groups of examples called bags.
Ten ways to fool the masses with machine learning
If you want to tell people the truth, make them laugh, otherwise they'll kill you.
A Generalized Meta-loss function for regression and classification using privileged information
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informative or privileged features which are available during training only to generate test predictions using input space features which are available both during training and testing.
Machine Learning with Abstention for Automated Liver Disease Diagnosis
For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect.
Training large margin host-pathogen protein-protein interaction predictors
We have also developed a web server for our HPI predictor called HoPItor (Host Pathogen Interaction predicTOR) that can predict interactions between human and viral proteins.
pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications
Motivation: A major challenge in the development of machine learning based methods in computational biology is that data may not be accurately labeled due to the time and resources required for experimentally annotating properties of proteins and DNA sequences.
