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Mass is one important suspicious object for breast cancer diagnosis in mammograms. Computer-aided detection (CAD) based on fully supervised deep learning achieves high performance for mass detection in mammograms. The lack of fine-grained expert labels becomes the bottleneck for the large-scale application of CAD to achieve detection in mammograms. Weakly supervised methods provide a solution to tackle the annotation problems, including in the application for mass detection. However, previous works face the problem of insufficient localization information, which affect the ability of mass detection. In this paper, we propose a multi-view enhancing mass detection network (MVMDNet) with dual view inputs that contains craniocaudal (CC) and mediolateral oblique (MLO) view of mammograms, where different view features are interacted and fused to enhance localization information.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.

The Fundamentals of Geometric Dimensioning and Tolerancing 2018 Using Critical Thinking Skills by Alex Krulikowski reflects the technical content found in the latest release of the ASME Y14.5-2018 Standard. This book includes several key features that aid in the understanding of geometric tolerancing. Each of the textbook's 26 chapters focuses on a major topic that must be mastered to be fluent in the fundamentals of GD&T. Each topic includes a goal that is defined and supported by a set of performance objectives that include real-world examples, verification principles and methods, and chapter summaries. There are more than 260 performance objectives that describe specific, observable, measurable actions that the student must accomplish to demonstrate mastery of each goal. Learning is reinforced by completing three types of exercise problems, along with critical thinking questions that promote application of GD&T on the job.