1.1k citations · 1.8k across the 28 of their papers we have counts for
50 papers
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…
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…
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…
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…