HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning

Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly-labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a view generation method for representation learning using joint embedding architectures that enhances representation learning for histology images. HistoPerm permutes augmented views of patches extracted from whole-slide histology images to improve classification performance. We evaluated the effectiveness of HistoPerm on two histology image datasets for Celiac disease and Renal Cell Carcinoma, using three widely used joint embedding architecture-based representation learning methods: BYOL, SimCLR, and VICReg. Our results show that HistoPerm consistently improves patch- and slide-level classification performance in terms of accuracy, F1-score, and AUC. Specifically, for patch-level classification accuracy on the Celiac disease dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal Cell Carcinoma dataset, patch-level classification accuracy is increased by 2% for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease dataset, models with HistoPerm outperform the fully-supervised baseline model by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for the models up to 10% relative to the fully-supervised baseline. These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully-supervised methods

STRIKE 3000 Electric Trike

The Strike 3000 is an electric, three-wheeled vehicle designed to revolutionize the way people travel. This vehicle will be the first of its kind to be designed for operation while standing rather than sitting like all current market designs. It is designed to create a healthier lifestyle to help combat the sedentary lives that many people are being forced to live, to be a comfortable, safe, and affordable mode of transportation for short-distance, everyday travel. It will include several cubic feet of storage space for things like groceries, small packages and other common cargo items. To be up to the leading edge of vehicle technology, this design also use a zero-emissions electric drive system using a 8.1kW LiFePO4 battery and a 10kW brush-less DC motor. The objective of this project is to design, test, and, at least in part, construct the vehicle by May 2017. Unfortunately, construction of the the vehicle was not implemented, but the design for the chassis and roll cage was finalized after consulting the Baja SAE 2017 rules and regulations and the NHTSA safety regulations, which were used as the primary design template along with the project sponsor, Ken Howes for aesthetic preferences. Once that was established, the components of the suspension system were designed based on the dimensions of the chassis. The steering system and the wheel knuckle were simultaneously researched and designed in the same manner. Because of unforeseen hurdles, time constraints, and underestimation of the costs of designing and building a vehicle prototype, other systems and components of the Strike have been re-prioritized and have not be receiving as much attention as it previously had. Instead how to go about continuing the design and build process is detailed in this report to provide constructive suggestions and recommendations to the future design teams of the Strike 3000

Improving Representation Learning for Histopathologic Images with Cluster Constraints

Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides - a process that is both labor-intensive and time-consuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset.Comment: Accepted by ICCV202

Green fabrication of stable lead-free bismuth based perovskite solar cells using a non-toxic solvent

The very fast evolution in certified efficiency of lead-halide organic-inorganic perovskite solar cells to 24.2%, on par and even surpassing the record for polycrystalline silicon solar cells (22.3%), bears the promise of a new era in photovoltaics and revitalisation of thin film solar cell technologies. However, the presence of toxic lead and particularly toxic solvents during the fabrication process makes large-scale manufacturing of perovskite solar cells challenging due to legislation and environment issues. For lead-free alternatives, non-toxic tin, antimony and bismuth based solar cells still rely on up-scalable fabrication processes that employ toxic solvents. Here we employ non-toxic methyl-acetate solution processed (CH3NH3)3Bi2I9 films to fabricate lead-free, bismuth based (CH3NH3)3Bi2I9 perovskites on mesoporous TiO2 architecture using a sustainable route. Optoelectronic characterization, X-ray diffraction and electron microscopy show that the route can provide homogeneous and good quality (CH3NH3)3Bi2I9 films. Fine-tuning the perovskite/hole transport layer interface by the use of conventional 2,2′,7,7′-tetrakis (N,N′-di-p-methoxyphenylamino)−9,9′-spirbiuorene, known as Spiro-OMeTAD, and poly(3-hexylthiophene-2,5-diyl - P3HT as hole transporting materials, yields power conversion efficiencies of 1.12% and 1.62% under 1 sun illumination. Devices prepared using poly(3-hexylthiophene-2,5-diyl hole transport layer shown 300 h of stability under continuous 1 sun illumination, without the use of an ultra violet-filter

Basic Pharmacology in Medicine

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BASIC PHARMACOLOGI IN MEDICINE

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Basic Pharmacology In Medicine

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