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

Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy

One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations. Existing IS methods usually fail in handling such scenarios, as they rely on coarse instance representations such as keypoints and horizontal bounding boxes (h-bboxes). In this paper, we propose a novel one-stage framework named A2B-IS to address this challenge and enhance the accuracy of IS in MS images. Our approach represents each instance with a pixel-level mask map and a rotated bounding box (r-bbox). Unlike two-stage methods that use box proposals for segmentations, our method decouples mask and box predictions, enabling simultaneous processing to streamline the model pipeline. Additionally, we introduce a Gaussian skeleton map to aid the IS task in two key ways: (1) It guides anchor placement, reducing computational costs while improving the model's capacity to learn RoI-aware features by filtering out noise from background regions. (2) It ensures accurate isolation of densely packed instances by rectifying erroneous box predictions near instance boundaries. To further enhance the performance, we integrate two modules into the framework: (1) An Atrous Attention Block (A2B) designed to extract high-resolution feature maps with fine-grained multiscale information, and (2) A Semi-Supervised Learning (SSL) strategy that leverages both labeled and unlabeled images for model training. Our method has been thoroughly validated on two large-scale MS datasets, demonstrating its superiority over most state-of-the-art approaches

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