GNNFairViz: Visual Analysis for Graph Neural Network Fairness.

Recent advancements in Graph Neural Networks (GNNs) show promise for various applications like social networks and financial networks. However, they exhibit fairness issues, particularly in human-related decision contexts, risking unfair treatment of groups historically subject to discrimination. While several visual analytics studies have explored fairness in machine learning (ML), few have tackled the particular challenges posed by GNNs.

Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries.

Purpose: In medical deep learning, models not trained from scratch are typically fine-tuned based on ImageNet-pretrained models. We posit that pretraining on data from the domain of the downstream task should almost always be preferable.

Materials And Methods: We leverage RadNet-12M and RadNet-1.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Integrating planetary health education into tertiary curricula: a practical toolbox for implementation.

Objective: To present a series of case studies from our respective countries and disciplines on approaches to implementing the Planetary Health Education Framework in university health professional education programs, and to propose a curriculum implementation and evaluation toolbox for educators to facilitate the adoption of similar initiatives in their programs. We emphasize the importance of applying an Indigenous lens to curriculum needs assessment, development, implementation, and evaluation.

Methods: Case studies from Australia and United States were collated using a six-stage design-based educational research framework (Focus, Formulation, Contextualization, Definition, Implementation, Evaluation) for teaching planetary health and methods of curriculum evaluation.

When, Where and How Does it Fail? A Spatial-Temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving.

Arguably the most representative application of artificial intelligence, autonomous driving systems usually rely on computer vision techniques to detect the situations of the external environment. Object detection underpins the ability of scene understanding in such systems. However, existing object detection algorithms often behave as a black box, so when a model fails, no information is available on When, Where and How the failure happened.

Computer-assisted navigation increases precision of component placement in total knee arthroplasty.

Unlabelled: In our clinical study, 200 total knee arthroplasties were evaluated to compare the use of the OrthoPilot system with conventional mechanical instrumentation. Long-term outcome of total knee replacement depends mainly on the accuracy of implant positioning and restoration of the mechanical leg axis. Our experience was that navigation could achieve a greater degree of accuracy concerning the aforementioned aspects.