Prevalence of uterine lesions associated with leiomyomas and role of endometrial biopsy in management

Background: Present study highlights association between symptomatic patients of fibroids and its coexistence with pathologies like endometriosis, adenomyosis, polyp, endometrial hyperplasia and carcinoma. The aim was evaluating role of endometrial biopsy before surgery.Methods: The study was observational cohort, conducted on women operated for fibroid or recently diagnosed with fibroid. 150 women were included. Histopathology reports of already operated were analysed for association between uterine pathology and fibroid. In prospective cases ultrasonography findings was noted and patients having leiomyoma underwent biopsy and reports studied for association. Chi-square test done to find association between qualitative variable and p value <0.05 considered significant.Results: Out of 150, 24.6% had adenomyosis, 14% had endometrial hyperplasia in which 2% had atypia and 12% without atypia, 8.6% had cervical polyp, 5.33% had endometrial polyp, 4% had endometriosis while 42.7% had no association.Conclusions: This study revealed increasing trend of coexistence of leiomyomas with uterine pathologies. Early identification of endometrial pathologies on clinical history and imaging helps in selection of high-risk patients who need biopsy to rule out malignancy thus avoiding routine D and C which is done for every case

Delayed drug hypersensitivity reaction to secukinumab in a patient with hidradenitis suppurativa

A woman in her 30s presented to the dermatology clinic with widespread, pruritic, red papules and plaques involving the ears, trunk and extremities. The rash developed a few days after receiving her second injection of secukinumab, which was initiated for recalcitrant Hurley stage III hidradenitis suppurativa. Investigations revealed a psoriasiform drug hypersensitivity reaction secondary to secukinumab. In this report, we describe the clinical course, histopathological correlation and treatment of this rarely documented reaction

Low-cost, multispectral imaging mini-microscope for longitudinal oximetry in small animals

We present a multispectral imaging mini-microscope for longitudinal oximetry in small animals. By replacing expensive and complex imaging systems using a low-cost imaging system

Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.Comment: 16 pages, 10 figures. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19

Multi-spectral vascular oximetry of rat dorsal spinal cord

We describe a visible-light multi-spectral system for vascular oximetry studies that can be implemented in lowand middle-income countries, using a low-cost electronics and optical elements, for instance a Raspberry Pi, a Pi camera under a resolution of 5-megapixel, 2592x1944-pixel resolution, and four different light sources at 480nm, 532nm, 593nm and 610nm on a singular structured illumination area. It is designed to quantify the vascular oxygen saturation change of the rat dorsal spinal cord, which uses a Phyton custom application that synchronize all elements to execute the imaging process in one system, powered by a portable rechargeable 5V battery pack. Aimed for drug discovery, tracking disease progression and understanding of progressive and degenerative diseases. By replacing expensive and bulky imaging systems

Phase and amplitude imaging with quantum correlations through Fourier Ptychography

Extracting as much information as possible about an object when probing with a limited number of photons is an important goal with applications from biology and security to metrology. Imaging with a few photons is a challenging task as the detector noise and stray light are then predominant, which precludes the use of conventional imaging methods. Quantum correlations between photon pairs has been exploited in a so called ‘heralded imaging scheme’ to eliminate this problem. However these implementations have so-far been limited to intensity imaging and the crucial phase information is lost in these methods. In this work, we propose a novel quantum-correlation enabled Fourier Ptychography technique, to capture high-resolution amplitude and phase images with a few photons. This is enabled by the heralding of single photons combined with Fourier ptychographic reconstruction. We provide experimental validation and discuss the advantages of our technique that include the possibility of reaching a higher signal to noise ratio and non-scanning Fourier Ptychographic acquisition

Low-cost, Multispectral Imaging Mini-microscope for Longitudinal Oximetry in Small Animals

No abstract available

Image Retrieval using Multi-scale CNN Features Pooling

In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.Comment: Accepted at ICMR 202