19 research outputs found
FROM USER-GENERATED-CONTENT TO STRUCTURED KNOWLEDGE EXPLORING MULTI-ASPECT SENTENCE REPRESENTATION AND PROTOTYPE HIERARCHY BASED CATEGORIZATION FOR ORGANIZATION OF TEXT COLLECTIONS
Ph.DDOCTOR OF PHILOSOPH
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
Multiparametric MR imaging in diagnosis of chronic prostatitis and its differentiation from prostate cancer
AbstractChronic prostatitis is a heterogeneous condition with high prevalence rate. Chronic prostatitis has overlap in clinical presentation with other prostate disorders and is one of the causes of high serum prostate specific antigen (PSA) level. Chronic prostatitis, unlike acute prostatitis, is difficult to diagnose reliably and accurately on the clinical grounds alone. Not only this, it is also challenging to differentiate chronic prostatitis from prostate cancer with imaging modalities like TRUS and conventional MR Imaging, as the findings can mimic those of prostate cancer. Even biopsy doesn't play promising role in the diagnosis of chronic prostatitis as it has limited sensitivity and specificity. As a result of this, chronic prostatitis may be misdiagnosed as a malignant condition and end up in aggressive surgical management resulting in increased morbidity. This warrants the need of reliable diagnostic tool which has ability not only to diagnose it reliably but also to differentiate it from the prostate cancer. Recently, it is suggested that multiparametric MR Imaging of the prostate could improve the diagnostic accuracy of the prostate cancer. This review is based on the critically published literature and aims to provide an overview of multiparamateric MRI techniques in the diagnosis of chronic prostatitis and its differentiation from prostate cancer
DietLens-eout: Large scale restaurant food photo recognition
10.1145/3323873.3326923ICMR 2019399-40
Mixed Dish Recognition through Multi-Label Learning
10.1145/3326458.3326929ICMR 20191-Au
Learning Using Privileged Information for Food Recognition
10.1145/3343031.3350870ACM MM 2019557-56
Heterogeneous Fusion of Semantic and Collaborative Information for Visually-Aware Food Recommendation
10.1145/3394171.3413598ACM Multimedia 202
Mixed-dish Recognition with Contextual Relation Networks
National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ