Multiple Myeloma Cancer Cell Instance Segmentation

Images remain the largest data source in the field of healthcare. But at the same time, they are the most difficult to analyze. More than often, these images are analyzed by human experts such as pathologists and physicians. But due to considerable variation in pathology and the potential fatigue of human experts, an automated solution is much needed. The recent advancement in Deep learning could help us achieve an efficient and economical solution for the same. In this research project, we focus on developing a Deep Learning-based solution for detecting Multiple Myeloma cancer cells using an Object Detection and Instance Segmentation System. We explore multiple existing solutions and architectures for the task of Object Detection and Instance Segmentation and try to leverage them and come up with a novel architecture to achieve comparable and competitive performance on the required task. To train our model to detect and segment Multiple Myeloma cancer cells, we utilize a dataset curated by us using microscopic images of cell slides provided by Dr.Ritu Gupta(Prof., Dept. of Oncology AIIMS).

PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability

Fashion is an important part of human experience. Events such as interviews, meetings, marriages, etc. are often based on clothing styles. The rise in the fashion industry and its effect on social influencing have made outfit compatibility a need. Thus, it necessitates an outfit compatibility model to aid people in clothing recommendation. However, due to the highly subjective nature of compatibility, it is necessary to account for personalization. Our paper devises an attribute-wise interpretable compatibility scheme with personal preference modelling which captures user-item interaction along with general item-item interaction. Our work solves the problem of interpretability in clothing matching by locating the discordant and harmonious attributes between fashion items. Extensive experiment results on IQON3000, a publicly available real-world dataset, verify the effectiveness of the proposed model.