70 citations · 232 across the 36 of their papers we have counts for
10 papers · 1 filter
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
Piotr Keller, Muhammad Dawood, Brinder Singh Chohan +1
Machine learning in computational pathology (CPath) often aggregates patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to generate WSI-level prediction scores…
REET: Robustness Evaluation and Enhancement Toolbox for Computational Pathology
Alex Foote, Amina Asif, Nasir Rajpoot +1
Motivation: Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in ra…
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
Fayyaz Minhas, Michael S. Toss, Noor ul Wahab +2
Can we predict if an early stage cancer patient is at high risk of developing distant metastasis and what clinicopathological factors are associated with such a risk? In this paper…
Learning Neural Activations
Fayyaz ul Amir Afsar Minhas, Amina Asif
An artificial neuron is modelled as a weighted summation followed by an activation function which determines its output. A wide variety of activation functions such as rectified li…
Generalized Learning with Rejection for Classification and Regression Problems
Amina Asif, Fayyaz ul Amir Afsar Minhas
Learning with rejection (LWR) allows development of machine learning systems with the ability to discard low confidence decisions generated by a prediction model. That is, just lik…
An embarrassingly simple approach to neural multiple instance classification
Amina Asif, Fayyaz ul Amir Afsar Minhas
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…
Ten ways to fool the masses with machine learning
Fayyaz Minhas, Amina Asif, Asa Ben-Hur
If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developin…
Machine Learning with Abstention for Automated Liver Disease Diagnosis
Kanza Hamid, Amina Asif, Wajid Abbasi +2
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning m…
A Generalized Meta-loss function for regression and classification using privileged information
Amina Asif, Muhammad Dawood, Fayyaz ul Amir Afsar Minhas
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informati…
pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications
Amina Asif, Wajid Arshad Abbasi, Farzeen Munir +2
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…