A study of wavelet-based noise reduction techniques in mammograms

Breast cancer is one of the most common cancers and claims over one thousand lives every day. Breast cancer turns fatal only when diagnosed in late stages, but can be cured when diagnosed in its early stages. Over the last two decades, Digital Mammography has served the diagnosis of breast cancer. It is a very powerful aid for early detection of breast cancer. However, the images produced by mammography typically contain a great amount noise from the inherent characteristics of the imaging system and the radiation involved. Shot noise or quantum noise is the most significant noise which emerges as a result of uneven distribution of incident photons on the receptor. The X-ray dose given to patients must be minimized because of the risk of exposure. This noise present in mammograms manifests itself more when the dose of X-ray radiation is less and therefore needs to be treated before enhancing the mammogram for contrast and clarity. Several approaches have been taken to reduce the amount of noise in mammograms. This thesis presents a study of the wavelet-based techniques employed for noise reduction in mammograms --Abstract, page iii

On Reinforcement Learning for Turn-based Zero-sum Markov Games

We consider the problem of finding Nash equilibrium for two-player turn-based zero-sum games. Inspired by the AlphaGo Zero (AGZ) algorithm, we develop a Reinforcement Learning based approach. Specifically, we propose Explore-Improve-Supervise (EIS) method that combines "exploration", "policy improvement"' and "supervised learning" to find the value function and policy associated with Nash equilibrium. We identify sufficient conditions for convergence and correctness for such an approach. For a concrete instance of EIS where random policy is used for "exploration", Monte-Carlo Tree Search is used for "policy improvement" and Nearest Neighbors is used for "supervised learning", we establish that this method finds an $\varepsilon$-approximate value function of Nash equilibrium in $\widetilde{O}(\varepsilon^{-(d+4)})$ steps when the underlying state-space of the game is continuous and $d$-dimensional. This is nearly optimal as we establish a lower bound of $\widetilde{\Omega}(\varepsilon^{-(d+2)})$ for any policy

Cecal Bascule after Colonoscopy - Case Report and Review of Literature

Cecal bascule is a rare disease variant of a cecal volvulus. It consists of upward and anterior folding of the ascending colon, forming a flap valve, and occluding the bowel lumen resulting in proximal cecal dilatation. Herein, we present a case of a patient who developed persistent abdominal pain few hours after a colonoscopy. CT scan of the abdomen revealed an upward and anterior folding of the cecum. Subsequently the patient was taken to the operating room for a right hemi-colectomy. This case emphasizes the importance to consider cecal bascule as a differential diagnosis in patients with persistent abdominal pain after colonoscopy, considering the ease of diagnosis with imaging studies and emergent surgical correction

A study of comparison of tension band wiring versus plating for olecranon fractures

Background: Olecranon fractures are one of the common fractures around the elbow, comprising around 37% of all fractures occurring around the elbow. Olecranon fractures are commonly treated with either plating or tension band wiring. The purpose of current study is to compare the clinical and radiological outcome of tension band wiring and plate fixation in patients operated for olecranon fractures.Methods: Current study was conducted in a tertiary care center from May 2017-2019. Study compromises of 30 patients operated for olecranon fractures. Clinical and radiological outcome of patients treated with tension band wiring or plating and assessed using the Mayo’s elbow score at 6 months follow up.Results: Out of the 30 patients, 15 were treated with tension band wiring and 15 were treated using open reduction and plating. Out of the 15 operated with tension-band wiring (TBW) K wire on follow up 11 showed excellent score on Mayo elbow score, 2 had good results and 2 had fair results. In patients operated with Plating 12 showed excellent result on follow up and 3 showed good result. No patient had fair or poor score.Conclusions: Both tension band wiring and plate fixation are effective methods for treatment of olecranon fractures however complications regarding symptomatic metal prominence and superficial infection were higher in patients treated with tension band wiring as compared to plate fixations

Prevalence of obesity in students with specific learning disorder in a metropolitan city of India

Background: Obesity is common in urban school children. Learning disability (LD) prevalence is also growing, primarily in cities. Objective: The objective of this study is to find the prevalence of obesity in students with specific LD (SLD). Materials and Methods: This observational cross-sectional study carried out at a tertiary care center attached to a medical college in Maharashtra, after obtaining permission from the institutional ethics committee. Consecutive 150 students with SLD between the ages of 8 and 18 years were studied over 18 months. Obesity was classified as per body mass index. Descriptive statistics and subgroup analysis were done by unpaired t-test. Results: Prevalence of obesity in students with SLD was 22.7% without genderpredisposition and family history correlation. Of total students with SLD, 44 (29.3%) had attention-deficit hyperactivity disorder (ADHD) without any association with the obesity. Conclusions: Family history, ADHD, gender, other medical conditions, and drug history have no correlation with regard to obesity in SLD. There is a further requirement of research with large population control size

ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs

Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adaptations (LoRA) have been proposed as a parameter-efficient way of achieving concept-driven personalization. While recent work explores the combination of separate LoRAs to achieve joint generation of learned styles and subjects, existing techniques do not reliably address the problem; they often compromise either subject fidelity or style fidelity. We propose ZipLoRA, a method to cheaply and effectively merge independently trained style and subject LoRAs in order to achieve generation of any user-provided subject in any user-provided style. Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize. Project page: https://ziplora.github.ioComment: Project page: https://ziplora.github.i

Behavior Optimized Image Generation

The last few years have witnessed great success on image generation, which has crossed the acceptance thresholds of aesthetics, making it directly applicable to personal and commercial applications. However, images, especially in marketing and advertising applications, are often created as a means to an end as opposed to just aesthetic concerns. The goal can be increasing sales, getting more clicks, likes, or image sales (in the case of stock businesses). Therefore, the generated images need to perform well on these key performance indicators (KPIs), in addition to being aesthetically good. In this paper, we make the first endeavor to answer the question of "How can one infuse the knowledge of the end-goal within the image generation process itself to create not just better-looking images but also "better-performing'' images?''. We propose BoigLLM, an LLM that understands both image content and user behavior. BoigLLM knows how an image should look to get a certain required KPI. We show that BoigLLM outperforms 13x larger models such as GPT-3.5 and GPT-4 in this task, demonstrating that while these state-of-the-art models can understand images, they lack information on how these images perform in the real world. To generate actual pixels of behavior-conditioned images, we train a diffusion-based model (BoigSD) to align with a proposed BoigLLM-defined reward. We show the performance of the overall pipeline on two datasets covering two different behaviors: a stock dataset with the number of forward actions as the KPI and a dataset containing tweets with the total likes as the KPI, denoted as BoigBench. To advance research in the direction of utility-driven image generation and understanding, we release BoigBench, a benchmark dataset containing 168 million enterprise tweets with their media, brand account names, time of post, and total likes