ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection

Open-vocabulary object detection (OVOD) aims to recognize novel objects whose categories are not included in the training set. In order to classify these unseen classes during training, many OVOD frameworks leverage the zero-shot capability of largely pretrained vision and language models, such as CLIP. To further improve generalization on the unseen novel classes, several approaches proposed to additionally train with pseudo region labeling on the external data sources that contain a substantial number of novel category labels beyond the existing training data. Albeit its simplicity, these pseudo-labeling methods still exhibit limited improvement with regard to the truly unseen novel classes that were not pseudo-labeled. In this paper, we present a novel, yet simple technique that helps generalization on the overall distribution of novel classes. Inspired by our observation that numerous novel classes reside within the convex hull constructed by the base (seen) classes in the CLIP embedding space, we propose to synthesize proxy-novel classes approximating novel classes via linear mixup between a pair of base classes. By training our detector with these synthetic proxy-novel classes, we effectively explore the embedding space of novel classes. The experimental results on various OVOD benchmarks such as LVIS and COCO demonstrate superior performance on novel classes compared to the other state-of-the-art methods. Code is available at https://github.com/clovaai/ProxyDet.Comment: Accepted in AAAI24. Code: https://github.com/clovaai/ProxyDet Project page: https://proxydet.github.i

Building a successful fertility preservation program at a major cancer center

Over 150,000 reproductive age individuals face fertility-threatening cancer treatments each year. Improved detection and treatment of cancer in reproductive-age patients have greatly increased the long-term survival and made it possible for these individuals to consider their long-term quality-of-life after cancer including having biologic offspring. Various methods of fertility preservation (FP) are now available for both males and females. In order to maximize FP options available to patients facing imminent gonadotoxic therapies, it is crucial that women have quick access to FP care and that providers expedite FP strategies. The overarching goal of a clinical FP program is to help patients and their physicians consider the impact of treatment on future fertility and facilitate FP efforts in what is often a limited time period before cancer treatment begins

Young female cancer survivors’ use of fertility care after completing cancer treatment

To investigate factors associated with female young adult cancer survivors’ (YCS) use of fertility care (FC), including consultation or fertility treatment, after completing their cancer treatment

Which patients pursue fertility preservation treatments? A multicenter analysis of the predictors of fertility preservation in women with breast cancer

To evaluate predictors of undergoing fertility preservation treatment (FPT) in women with breast cancer

Polycystic ovarian syndrome (PCOS), related symptoms/sequelae, and breast cancer risk in a population-based case–control study

Despite the overlap between the clinical symptoms/sequelae of polycystic ovarian syndrome (PCOS) and many known reproductive risk factors for breast cancer, the relationship between PCOS and breast cancer remains unclear, possibly because of the complex heterogeneity and challenges in diagnosing PCOS over time. We hypothesized that PCOS, specific PCOS-related symptoms/sequelae, or clusters of PCOS-related symptoms/sequelae, may be differentially associated with pre- vs. postmenopausal breast cancer risk

Fertility preservation in patients with haematological disorders: a retrospective cohort study

This study investigated the factors associated with utilization of fertility preservation and the differences in treatments and outcomes by prior chemotherapy exposure in patients with haematological diseases. This study included all 67 women with haematological diseases seen for fertility preservation consultation at two university hospitals between 2006 and 2011. Of the total, 49% had lymphoma, 33% had leukaemia, 7% had myelodysplastic syndrome and 4% had aplastic anaemia; 46% had prior chemotherapy; and 33% were planning for bone marrow transplantation, 33% pursued ovarian stimulation and 7% used ovarian tissue banking; and 48% of patients did not pursue fertility preservation treatment. All five cycle cancellations were in the post-chemotherapy group: three patients with leukaemia and two with lymphoma. Patients with prior chemotherapy had lower baseline antral follicle count (10 versus 22) and received more gonadotrophins to achieve similar peak oestradiol concentrations, with no difference in oocyte yield (10.5 versus 10) after adjustment for age. Embryo yield was similar between those who had prior chemotherapy and those who had not. Half of the patients with haematological diseases who present for fertility preservation have been exposed to chemotherapy. While ovarian reserve is likely impaired in this group, oocyte yield may be acceptable

“Being Guided”: What Oncofertility Patients’ Decisions Can Teach Us About the Efficacy of Autonomy, Agency, and Decision-Making Theory in the Contemporary Critical Encounter

Recent research on patient decision-making reveals a disconnect between theories of autonomy, agency, and decision-making and their practice in contemporary clinical encounters. This study examines these concepts in the context of female patients making oncofertility decisions in the United Kingdom in light of the phenomenon of “being guided.” Patients experience being guided as a way to cope with, understand, and defer difficult treatment decisions. Previous discussions condemn guided decision-making, but this research suggests that patients make an informed, autonomous decision to be guided by doctors. Thus, bioethicists must consider the multifaceted ways that patients enact their autonomy in medical encounters

The disruptive impact of technology on the fashion industry

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 47-50).The rules of the power game are shifting in the fashion industry due to technology. High-end fashion brands are faced with a significant disruption because of the forces of media and technology. Traditionally, fashion trends trickled down from high-end fashion to lower-end fashion or mass fashion. High-end luxury fashion brands once wielded absolute power as trendsetters, and mass brands followed the trends set by exclusive brands. Traditional high-end print fashion magazines such as Vogue and Harper 's Bazaar were powerful sources of information about upcoming fashion trends. However, technology is changing every element of this power structure. Luxury brands and highend fashion magazines no longer have the absolute power as fashion trendsetters that they once enjoyed. In this new era, the ability to adapt to digital and technology trends will be the key solution for fashion brands to maintain their traditional power. This thesis is an exploration into three sectors of disruption: Fashion Week Cycle, Fashion Communication, and Fashion Retailers. In this thesis, I analyze and discuss how technology has been disrupting the fashion industry and suggest possible solutions and strategies for traditional players who want to regain their standing.by Jayeon Kim.M.B.A

An Alternative Model of Type A Dependence in a Gene Set of Correlated Genes

Klebanov et al. (2006) proposed a new type of stochastic dependence, Type A dependence, between gene expression levels. They estimated the abundance of Type A pairs by testing the correlation coefficients of gene pairs. We propose a new model, hidden regulator dependence, as an alternative to Type A dependence. We show that the correlation based procedure proposed by Klebanov et al. (2006) fails to differentiate hidden regulator dependence from Type A dependence, although their probabilistic structures are quite different.