Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201

Developing an undergraduate information studies curriculum in support of social justice

Through a review of the current state of Library and Information Studies (LIS) undergraduate education and the orientation of sample programs at consortium member iSchools, this article proposes an alternative pedagogical model that foregrounds the integration of a critical, social justice framework into undergraduate curricula. Based on an initial study conducted at the University of California, Los Angeles (UCLA) by the authors and other participants of the Winter/Spring 2015 Teacher Training Seminar, this essay, in addition to positing an approach towards the building of critically engaged undergraduate curriculum, moreover highlights the increasing need and support for LIS students and professionals that have a nuanced information praxis. Inspired by UCLA’s own commitment to a social justice orientation, the authors also point towards existing advocacy for social justice in LIS undergraduate education in the field and professional literature. In turn, underscoring the broader movement for a critical education and practice within LIS

Revisiting a Feminist Ethics of Care in Archives: An Introductory Note

In this featured commentary, Michelle Caswell and Marika Cifor revisit their article, “From Human Rights to Feminist Ethics: Radical Empathy in Archives”* and update its insights to reflect care work in our present time of crisis. Pre-print first published online 06/11/2021   *Michelle Caswell and Marika Cifor, “From Human Rights to Feminist Ethics: Radical Empathy in Archives,” Archivaria 81 (Spring 2016): 23-43

Inscribing Gender: A Duoethnographic Examination of Gendered Values and Practices in Fitness Tracker Design

Using fitness trackers to generate and collect quantifiable data is a widespread practice aimed at better understanding one’s health and body. The intentional design of fitness trackers as genderless or universal is predicated on masculinist design values and assumptions and does not result in “neutral” artifacts. Instead, ignoring gender in the design of fitness tracking devices marks a dangerous ongoing inattention to the needs, desires, lives, and life chances of women, as well as transgender and gender non-conforming persons. We utilize duoethnography, a methodology emphasizing personal narrative and dialogue, as a tool that promotes feminist reflexivity in the design and study of fitness tracking technologies. Using the Jawbone UP3 as our object of study, we present findings that illustrate the gendered physical and interface design features and discuss how these features reproduce narrow understandings of gender, health, and lived experiences