The rising incidence of kidney cancer underscores the need for precise and reproducible diagnostic methods. In particular, renal cell carcinoma (RCC), the most prevalent type of kidney cancer, requires accurate nuclear grading for better prognostic prediction. Recent advances in deep learning have facilitated end-to-end diagnostic methods using contextual features in histopathological images. However, most existing methods focus only on image-level features or lack an effective process for aggregating nuclei prediction results, limiting their diagnostic accuracy. In this paper, we introduce a novel framework, Nuclei feature Assisted Patch-level RCC grading (NuAP-RCC), that leverages nuclei-level features for enhanced patch-level RCC grading. Our approach employs a nuclei-level RCC grading network to extract grade-aware features, which serve as node features in a graph. These node features are aggregated using graph neural networks to capture the morphological characteristics and distributions of the nuclei. The aggregated features are then combined with global image-level features extracted by convolutional neural networks, resulting in a final feature for accurate RCC grading. In addition, we present a new dataset for patch-level RCC grading. Experimental results demonstrate the superior accuracy and generalizability of NuAP-RCC across datasets from different medical institutions, achieving a 6.15% improvement in accuracy over the second-best model on the USM-RCC dataset.

Vision Transformers have demonstrated remarkable performance in various vision tasks. However, general-purpose processors, such as CPUs and GPUs, face challenges in efficiently handling the inference of Vision Transformers. To address the issue, prior works have focused on accelerating only attention due to its high computational cost in NLP Transformers. In contrast, Vision Transformers demonstrate a higher computational cost due to linear modules such as linear transformation, linear projection and Feed-Forward Network (FFN), compared to attention. In this paper, we present ViT-slice, an algorithm-architecture co-design that enhances end-to-end performance and energy efficiency by optimizing not only attention but also linear modules. At the algorithm level, we propose bit-slice compression that avoids storing the redundant most significant bits (MSBs). Additionally, we present bit-slice dot product with early skip to efficiently compute the dot product using bit-sliced data. To enable early skip during the dot product computation, we leverage a trainable threshold. On the hardware level, we introduce a specialized bit-slice dot product unit (BSDPU) to efficiently process the bit-slice dot product with early skip algorithm. Additionally, we present a bit-slice encoder and decoder for on-chip bit-slice compression. ViT-slice achieves 244×, 35.3×, 16.8×, 10.4×, 5.0× end-to-end speedup over Xeon CPU, EdgeGPU, TITAN Xp GPU, Sanger accelerator and ViTCoD accelerator, respectively.

Android application (app) stores contain ahugenumber of apps, which aremanuallyclassified based on the apps’ descriptions into various categories. However, the predefined categories or apps descriptions are usuallynotvery accurate to reflect the real functionalities of apps, thereby leading tomisclassifythe apps, which may cause serioussecurity issuesandunreliabilityproblem in the app store. Therefore, the automatic app classification is animportantdemand to construct asecure,reliable,integrated, andeasy to navigateapp store. In this paper, we propose an effective method calledAndroClasstoautomaticallyclassify apps based on theirrealfunctionalities by usingrichandcomprehensivefeatures representing theactualfunctionalities of the apps. AndroClass performsthreesteps offeature extraction,feature refinement, andclassification. In the feature extraction step, we extract 14 various features for each app by utilizing aunified tool suite. In the feature refinement step, we applyRandom Forestalgorithm to refine the features. In the classification step, we combine refined features into asingleone and AndroClass is equipped with K‐Nearest Neighbor, Naive Bayes, Support Vector Machine, and Deep Neural Network to classify apps. On the contrary to the existing methods, all the utilized features in AndroClass arestableandclearlyrepresent the actual functionalities of the app, AndroClass doesnotpose any issues to theuser privacy, and our method can be applied to classifyunreleasedornewly releasedapps. The results ofextensiveexperiments with tworeal-worlddatasets and a dataset constructed byhuman expertsdemonstrate the effectiveness of AndroClass where the classification accuracy of AndroClass with the latter dataset is 83.5%.