Asma Khan | Searcharxiv

arXiv2024

On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling

Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphology-based diagnostics and surgical planning. However, the effectiveness of SSMs is often constrained by the necessity for expert-driven manual segmentation, a time-intensive and expensive process that restricts their broader utility. While deep learning approaches offer a potential workaround by directly estimating SSMs from unsegmented images, they merely shift the burden. Although these models do not require segmentation during deployment, they still fail to address the challenge of acquiring the manual annotations needed for training, particularly in resource-limited settings. Semi-supervised models for anatomy segmentation present a logical solution to the annotation burden. However, the lack of established guidelines leaves end-users uncertain about the actual effectiveness of these approaches for the downstream task of constructing SSMs. In this study, we bridge this gap by systematically evaluating semi-supervised methods as viable alternatives to manual segmentation. By applying these methods under low-annotation settings and utilizing the predicted segmentations for SSM generation, we establish a comprehensive new performance benchmark. Our findings reveal a clear divide in performance: while certain methods yield noisy segmentations that degrade SSM quality, others accurately capture the population's modes of variation comparable to those obtained from manual-segmentation SSMs despite a 60-80% reduction in manual annotation requirements.

cs.CV↗

arXiv2021

NPE: An FPGA-based Overlay Processor for Natural Language Processing

In recent years, transformer-based models have shown state-of-the-art results for Natural Language Processing (NLP). In particular, the introduction of the BERT language model brought with it breakthroughs in tasks such as question answering and natural language inference, advancing applications that allow humans to interact naturally with embedded devices. FPGA-based overlay processors have been shown as effective solutions for edge image and video processing applications, which mostly rely on low precision linear matrix operations. In contrast, transformer-based NLP techniques employ a variety of higher precision nonlinear operations with significantly higher frequency. We present NPE, an FPGA-based overlay processor that can efficiently execute a variety of NLP models. NPE offers software-like programmability to the end user and, unlike FPGA designs that implement specialized accelerators for each nonlinear function, can be upgraded for future NLP models without requiring reconfiguration. We demonstrate that NPE can meet real-time conversational AI latency targets for the BERT language model with $4\times$ lower power than CPUs and $6\times$ lower power than GPUs. We also show NPE uses $3\times$ fewer FPGA resources relative to comparable BERT network-specific accelerators in the literature. NPE provides a cost-effective and power-efficient FPGA-based solution for Natural Language Processing at the edge.

cs.AR↗