7 citations · 20 across the 6 of their papers we have counts for
7 papers
Generative appearance replay for continual unsupervised domain adaptation
Boqi Chen, Kevin Thandiackal, Pushpak Pati +1
Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may beco…
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
Kevin Thandiackal, Luigi Piccinelli, Pushpak Pati +1
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data. Especially in medical disciplin…
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Kevin Thandiackal, Boqi Chen, Pushpak Pati +4
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate…
Generative Feature-driven Image Replay for Continual Learning
Kevin Thandiackal, Tiziano Portenier, Andrea Giovannini +2
Neural networks are prone to catastrophic forgetting when trained incrementally on different tasks. Popular incremental learning methods mitigate such forgetting by retaining a sub…
Artificial Intelligence Decision Support for Medical Triage
Chiara Marchiori, Douglas Dykeman, Ivan Girardi +6
Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and…
Marginal loss and exclusion loss for partially supervised multi-organ segmentation
Gonglei Shi, Li Xiao, Yang Chen +1
Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partial…