Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

Flow cytometric fluorescence pulse width analysis of etoposide-induced nuclear enlargement in HCT116 cells

Fluorescence pulse width can provide size information on the fluorescence-emitting particle, such as the nuclei of propidium iodide-stained cells. To analyze nuclear size in the present study, rather than perform the simple doublet discrimination approach usually employed in flow cytometric DNA content analyses, we assessed the pulse width of the propidium iodide fluorescence signal. The anti-cancer drug etoposide is reportedly cytostatic, can induce a strong G2/M arrest, and results in nuclear enlargement. Based on these characteristics, we used etoposide-treated HCT116 cells as our experimental model system. The fluorescence pulse widths (FL2-W) of etoposide-treated (10 μM, 48 h) cells were distributed at higher positions than those of vehicle control, so the peak FL2-W value of etoposide-treated cells appeared at 400 while those of vehicle control cells appeared at 200 and 270. These results were consistent with our microscopic observations. This etoposide-induced increase in FL2-W was more apparent in G2/M phase than other cell cycle phases, suggesting that etoposide-induced nuclear enlargement preferentially occurred in G2/M phase cells rather than in G0/G1 or S phase cells

Temperature-Aware Runtime Power Management for Chip-Multiprocessors with 3-D Stacked Cache

The advent of 3-D fabrication technology makes it possible to stack a large amount of last-level cache memory onto a multi-core die to reduce off-chip memory accesses and, thus, increases system performance. However, the higher power density (i.e., power dissipation per unit volume) of 3-D integrated circuits (ICs) might incur temperature-related problems in reliability, leakage power, system performance, and cooling cost. In this paper, we propose a runtime solution to maximize the performance (i.e., instruction throughput) of chip-multiprocessors with 3-D stacked last-level cache memory, without thermal-constraint violation. The proposed method combines runtime cache tuning (e.g., cache-way partitioning, cache-way power-gating, cache data placement) with per-core dynamic voltage/frequency scaling (DVFS) in a temperature-aware manner. Experimental results show that the integrated method offers 23% performance improvement on average in terms of instructions per second (IPS) compared with temperature-aware runtime cache tuning only

Imbalanced loss-integrated deep-learning-based ultrasound image analysis for diagnosis of rotator-cuff tear

A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Although UI offers comparable performance at a lower cost to other diagnostic instruments such as MRI, speckle noise can occur the degradation of the image resolution. Conventional vision-based algorithms exhibit inferior performance for the segmentation of diseased regions in UI. In order to achieve a better segmentation for diseased regions in UI, deep-learning-based diagnostic algorithms have been developed. However, it has not yet reached an acceptable level of performance for application in orthopedic surgeries. In this study, we developed a novel end-to-end fully convolutional neural network, denoted as Segmentation Model Adopting a pRe-trained Classification Architecture (SMART-CA), with a novel integrated on positive loss function (IPLF) to accurately diagnose the locations of RCT during an orthopedic examination using UI. Using the pre-trained network, SMART-CA can extract remarkably distinct features that cannot be extracted with a normal encoder. Therefore, it can improve the accuracy of segmentation. In addition, unlike other conventional loss functions, which are not suited for the optimization of deep learning models with an imbalanced dataset such as the RCT dataset, IPLF can efficiently optimize the SMART-CA. Experimental results have shown that SMART-CA offers an improved precision, recall, and dice coefficient of 0.604% (+38.4%), 0.942% (+14.0%) and 0.736% (+38.6%) respectively. The RCT segmentation from a normal ultrasound image offers the improved precision, recall, and dice coefficient of 0.337% (+22.5%), 0.860% (+15.8%) and 0.484% (+28.5%), respectively, in the RCT segmentation from an ultrasound image with severe speckle noise. The experimental results demonstrated the IPLF outperforms other conventional loss functions, and the proposed SMART-CA optimized with the IPLF showed better performance than other state-of-the-art networks for the RCT segmentation with high robustness to speckle noise. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Wide-Field 3D Ultrasound Imaging Platform with a Semi-Automatic 3D Segmentation Algorithm for Quantitative Analysis of Rotator Cuff Tears

Rotator cuff tear (RCT) is a common injury that causes pain and disability in adults. The quantitative diagnosis of the RCT can be crucial in determining a treatment plan or monitoring treatment efficacy. Currently, only a few diagnosis tools, such as magnetic resonance imaging (MRI) and ultrasound imaging (US), are utilized for the diagnosis. Specifically, US exhibited comparable performance with MRI while offering a readily available diagnosis of RCTs at a lower cost. However, three-dimensional(3D) US and analysis of the regions are necessary to enable a better diagnosis of RCTs. Therefore, we developed a wide-field 3D US platform with a semi-automatic 3D image segmentation algorithm for 3D quantitative diagnosis of RCTs. The 3D US platform is built based on a conventional 2D US system and obtains 3D US images via linear scanning. With respect to 3D segmentation algorithm based on active contour model, frequency compounding and anisotropic diffusion methods were applied, and their effects on segmentation were discussed. The platform was used for clinical examination after evaluating the platform via the RCT-mimicking phantoms. As verified by the Dice coefficient(average DC: 0.663, volume DC: 0.723), which was approximately up to 50% higher than that obtained with conventional algorithms, the RCT regions segmented by the developed algorithm significantly matched the ground truth. The results indicated that the wide-field 3D US platform with the 3D segmentation algorithm can constitute a useful tool for improving the accuracy in the diagnosis of RCTs, and can eventually lead to better determination of treatment plans and surgical planning.1

Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap

A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.1

A Power-Efficient 3-D On-Chip Interconnect for Multi-Core Accelerators with Stacked L2 Cache

The use of multi-core clusters is a promising option for data-intensive embedded applications such as multi-modal sensor fusion, image understanding, mobile augmented reality. In this paper, we propose a power-efficient 3-D on-chip interconnect for multi-core clusters with stacked L2 cache memory. A new switch design makes a circuit-switched Mesh-of-Tree (MoT) interconnect reconfigurable to support power-gating of processing cores, memory blocks, and unnecessary interconnect resources (routing switch, arbitration switch, inverters placed along the on-chip wires). The proposed 3-D MoT improves the power efficiency up to 77% in terms of energy-delay product (EDP)

Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media

Otitis media (OM) is one of the most common ear diseases in children and a common reason for outpatient visits to medical doctors in primary care practices. Adhesive OM (AdOM) is recognized as a sequela of OM with effusion (OME) and often requires surgical intervention. OME and AdOM exhibit similar symptoms, and it is difficult to distinguish between them using a conventional otoscope in a primary care unit. The accuracy of the diagnosis is highly dependent on the experience of the examiner. The development of an advanced otoscope with less variation in diagnostic accuracy by the examiner is crucial for a more accurate diagnosis. Thus, we developed an intelligent smartphone-based multimode imaging otoscope for better diagnosis of OM, even in mobile environments. The system offers spectral and autofluorescence imaging of the tympanic membrane using a smartphone attached to the developed multimode imaging module. Moreover, it is capable of intelligent analysis for distinguishing between normal, OME, and AdOM ears using a machine learning algorithm. Using the developed system, we examined the ears of 69 patients to assess their performance for distinguishing between normal, OME, and AdOM ears. In the classification of ear diseases, the multimode system based on machine learning analysis performed better in terms of accuracy and F1 scores than single RGB image analysis, RGB/fluorescence image analysis, and the analysis of spectral image cubes only, respectively. These results demonstrate that the intelligent multimode diagnostic capability of an otoscope would be beneficial for better diagnosis and management of OM. © 2021 OSA - The Optical Society. All rights reserved.1