48 citations · 99 across the 16 of their papers we have counts for
17 papers
Least-Ambiguous Multi-Label Classifier
Misgina Tsighe Hagos, Claes Lundström
Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, o…
Performance of Conformal Prediction in Capturing Aleatoric Uncertainty
Misgina Tsighe Hagos, Claes Lundström
Conformal prediction is a model-agnostic approach to generating prediction sets that cover the true class with a high probability. Although its prediction set size is expected to c…
Validation of Conformal Prediction in Cervical Atypia Classification
Misgina Tsighe Hagos, Antti Suutala, Dmitrii Bychkov +6
Deep learning based cervical cancer classification can potentially increase access to screening in low-resource regions. However, deep learning models are often overconfident and d…
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor +1
Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million people worldwide. Currently, radiologists grade the severity of OA on an ordinal scale from zero to fo…
Distance-Aware eXplanation Based Learning
Misgina Tsighe Hagos, Niamh Belton, Kathleen M. Curran +1
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL…
Unlearning Spurious Correlations in Chest X-ray Classification
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achievi…
Learning from Exemplary Explanations
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although…
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
Misgina Tsighe Hagos, Niamh Belton, Ronan P. Killeen +2
Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the…
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor +1
Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of trai…
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model. We present a simple method to identify spu…
Impact of Feedback Type on Explanatory Interactive Learning
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
Explanatory Interactive Learning (XIL) collects user feedback on visual model explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario. Different us…
Posture Prediction for Healthy Sitting using a Smart Chair
Tariku Adane Gelaw, Misgina Tsighe Hagos
Poor sitting habits have been identified as a risk factor to musculoskeletal disorders and lower back pain especially on the elderly, disabled people, and office workers. In the cu…
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability
Niamh Belton, Ivan Welaratne, Adil Dahlan +4
This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance I…
Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation
Misgina Tsighe Hagos
Introducing automated Diabetic Retinopathy (DR) diagnosis into Ethiopia is still a challenging task, despite recent reports that present trained Deep Learning (DL) based DR classif…
Automated Smartphone based System for Diagnosis of Diabetic Retinopathy
Misgina Tsighe Hagos, Shri Kant, Surayya Ado Bala
Early diagnosis of diabetic retinopathy for treatment of the disease has been failing to reach diabetic people living in rural areas. Shortage of trained ophthalmologists, limited…
Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach
Misgina Tsighe Hagos
Although deep learning research and applications have grown rapidly over the past decade, it has shown limitation in healthcare applications and its reachability to people in remot…
Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
Misgina Tsighe Hagos, Shri Kant
Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems. Transfer learning from an already train…