1.1k citations · 2.7k across the 92 of their papers we have counts for
21 papers · 1 filter
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Arij Bouazizi, Katharina Ernst +2
In microscopy image cell segmentation, it is common to train a deep neural network on source data, containing different types of microscopy images, and then fine-tune it using a su…
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Yuyuan Liu, Choubo Ding, Yu Tian +4
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on th…
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels
Brandon Smart, Gustavo Carneiro
Many state-of-the-art noisy-label learning methods rely on learning mechanisms that estimate the samples' clean labels during training and discard their original noisy labels. Howe…
Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation
Yuanhong Chen, Hu Wang, Chong Wang +6
When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-obli…
On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness
Adrian Galdran, Gustavo Carneiro, Miguel Ángel González Ballester
We study the impact of different loss functions on lesion segmentation from medical images. Although the Cross-Entropy (CE) loss is the most popular option when dealing with natura…
Instance-Dependent Noisy Label Learning via Graphical Modelling
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymm…
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models
Chong Wang, Yuanhong Chen, Yuyuan Liu +6
State-of-the-art (SOTA) deep learning mammogram classifiers, trained with weakly-labelled images, often rely on global models that produce predictions with limited interpretability…
An Evolutionary Approach for Creating of Diverse Classifier Ensembles
Alvaro R. Ferreira, Fabio A. Faria, Gustavo Carneiro +1
Classification is one of the most studied tasks in data mining and machine learning areas and many works in the literature have been presented to solve classification problems for…
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Katharina Ernst, Gustavo Carneiro +1
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consumi…
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels
Emeson Santana, Gustavo Carneiro, Filipe R. Cordeiro
Label noise is common in large real-world datasets, and its presence harms the training process of deep neural networks. Although several works have focused on the training strateg…
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios +2
Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced…
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Hu Wang, Jianpeng Zhang, Yuanhong Chen +4
Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process. However, this equal combination can be detrimenta…
Test Time Transform Prediction for Open Set Histopathological Image Recognition
Adrian Galdran, Katherine J. Hewitt, Narmin L. Ghaffari +3
Tissue typology annotation in Whole Slide histological images is a complex and tedious, yet necessary task for the development of computational pathology models. We propose to addr…
Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images
Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1
Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of sur…
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Luiz H. Buris, Daniel C. G. Pedronette, Joao P. Papa +3
Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real ap…
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
Yuyuan Liu, Yu Tian, Chong Wang +4
3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given t…
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
Yu Tian, Guansong Pang, Fengbei Liu +5
Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutiv…
Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder
Yu Tian, Guansong Pang, Yuyuan Liu +7
Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on re…
BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification
Yuanhong Chen, Fengbei Liu, Hu Wang +4
Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually a…
In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos
David Butler, Yuan Zhang, Tim Chen +3
Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the…
Mutual information neural estimation for unsupervised multi-modal registration of brain images
Gerard Snaauw, Michele Sasdelli, Gabriel Maicas +4
Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based regis…