The transition in medical image segmentation from fine-grained to coarse-grained annotation methods, notably scribble annotation, offers a practical and efficient preparation for deep learning applications. However, these methods often compromise segmentation precision and result in irregular contours. This study targets the enhancement of scribble-supervised segmentation to match the accuracy of fine-grained annotation. Capitalizing on the consistency of target shapes across unpaired datasets, this study introduces a shape-aware scribble-supervised learning framework (MaskMixAdv) addressing two critical tasks: (1) Pseudo label generation, where a mixup-based masking strategy enables image-level and feature-level data augmentation to enrich coarse-grained scribbles annotations. A dual-branch siamese network is proposed to generate fine-grained pseudo labels. (2) Pseudo label optimization, where a CNN-based discriminator is proposed to refine pseudo label contours by distinguishing them from external unpaired masks during model fine-tuning. MaskMixAdv works under constrained annotation conditions as a label-efficient learning approach for medical image segmentation. A case study on public cardiac MRI datasets demonstrated that the proposed MaskMixAdv outperformed the state-of-the-art methods and narrowed the performance gap between scribble-supervised and mask-supervised segmentation. This innovation cuts annotation time by at least 95%, with only a minor impact on Dice performance, specifically a 2.6% reduction. The experimental outcomes indicate that employing efficient and cost-effective scribble annotation can achieve high segmentation accuracy, significantly reducing the typical requirement for fine-grained annotations.

Value prediction is a microarchitectural technique that enhances processor performance by speculatively breaking true data dependencies. It has demonstrated improved performance in both single-threaded and multi-threaded workloads, rendering it an appealing microarchitectural approach. While high-performance value predictors can achieve impressive accuracy, they may also incur significant costs in terms of area, power consumption, and complexity. Therefore, there is a demand for lightweight value prediction techniques capable of striking a favorable balance between performance and overhead. However, designing value predictors with superior performance using limited resources presents an urgent challenge. Consequently, this work proposes a design space exploration framework for the state-of-the-art EVES value predictor, aiming to efficiently configure the design parameters of the value predictor within constrained RAM resources. Additionally, the article evaluates the performance of the explored value predictor across a wide range of workloads. The explored value predictors exhibit high efficiency across RAM sizes ranging from 2KB to 16KB while maintaining acceptable computational complexity. Furthermore, the results indicate that the explored value predictor achieves optimal efficiency under the 2KB constraint, with the highest acceleration-to-cost ratio reaching 4.02%/KB.