activity
20192026
most citedSCGC : Self-Supervised Contrastive Graph Clustering

14 citations · 38 across the 23 of their papers we have counts for

collaborators
Showing 2022Show all

9 papers · 1 filter

cs.LG2022

NBC-Softmax : Darkweb Author fingerprinting and migration tracking

Gayan K. Kulatilleke, Shekhar S. Chandra, Marius Portmann

Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, w…

eess.IV2022★ 1 cited

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative

Boyeong Woo, Craig Engstrom, William Baresic +3

In medical image analysis, automated segmentation of multi-component anatomical structures, which often have a spectrum of potential anomalies and pathologies, is a challenging tas…

eess.IV2022★ 2 cited

Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data

Linfeng Liu, Siyu Liu, Lu Zhang +3

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using mu…

cs.LG2022

Efficient block contrastive learning via parameter-free meta-node approximation

Gayan K. Kulatilleke, Marius Portmann, Shekhar S. Chandra

Contrastive learning has recently achieved remarkable success in many domains including graphs. However contrastive loss, especially for graphs, requires a large number of negative…

eess.IV2022

Structure Guided Manifolds for Discovery of Disease Characteristics

Siyu Liu, Linfeng Liu, Xuan Vinh +4

In medical image analysis, the subtle visual characteristics of many diseases are challenging to discern, particularly due to the lack of paired data. For example, in mild Alzheime…

cs.CV2022★ 1 cited

Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings

Zhen Yu, Toan Nguyen, Yaniv Gal +7

In practice, many medical datasets have an underlying taxonomy defined over the disease label space. However, existing classification algorithms for medical diagnoses often assume…