Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

ACLS: Adaptive and Conditional Label Smoothing for Network Calibration

We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based method that exploits a regularization term to smooth the miscalibrated confidences. Although these approaches have shown the effectiveness on calibrating the networks, there is still a lack of understanding on the underlying principles of regularization in terms of network calibration. We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration. Specifically, we have observed that 1) the regularization-based methods can be interpreted as variants of label smoothing, and 2) they do not always behave desirably. Based on the analysis, we introduce a novel loss function, dubbed ACLS, that unifies the merits of existing regularization methods, while avoiding the limitations. We show extensive experimental results for image classification and semantic segmentation on standard benchmarks, including CIFAR10, Tiny-ImageNet, ImageNet, and PASCAL VOC, demonstrating the effectiveness of our loss function.Comment: Accepted to ICCV 2023 (Oral presentation

Interpretable pap smear cell representation for cervical cancer screening

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.Comment: 20 pages, 6 figure

Practical photoacoustic tomography: Realistic limitations and technical solutions

This article offers a perspective on photoacoustic tomography (PAT) under realistic scenarios. While PAT has gained much attention in preclinical and clinical research, most early works used image reconstruction techniques based on ideal assumptions, and thus these techniques may not be fully effective in real environments. In this work, we consider such non-ideal conditions as a limited view, limited bandwidth, lossy medium, or heterogeneous medium. More importantly, we use k-Wave simulation to numerically evaluate the effects of these limiting factors on various image reconstruction algorithms. Then, to enable more reliable PAT image reconstruction, we introduce recent techniques for mitigating each of the limiting conditions. We seek to emphasize the importance of working within these realistic limitations, and we encourage researchers to develop compensating solutions that advance PAT's translation to real clinical environments.11Nsciescopu

Comparison of various photoacoustic imaging reconstruction algorithms under realistic scenarios: a simulation study

Undoubtedly, photoacoustic tomography (PAT) is a promising technique unveiling physiological information in the biomedical imaging field. However, targeting optically/acoustically non-uniform biological tissues and receiving signals with finite aperture/band-limited ultrasonic transducers have been practical limitations that hinder the clinical application of PAT. This study closely analyzes the effects of four main limiting factors named limited view (LV), limited bandwidth (LB), lossy medium (LM), and heterogeneous medium (HM) and compared the adverse effects given from the four factors. First, radiofrequency (RF) data was generated assuming a realistic situation using k-Wave, a MATLAB-based wave propagation-simulating tool. A single 4 mm-sized circular target was used, and physical parameters of the detector array were designed referring to a commercial linear probe (GE9LD, General Electric). Supposing double-aperture, full-band signal reception from the lossless, homogeneous medium as a baseline, RF data were acquired under a successive accumulation of the limiting factors in order of LV, LB, LM, and HM. Second, the obtained RF datasets were reconstructed with 7 representative beamforming algorithms; delay and sum (DAS), delay multiply and sum, pth root DAS, minimum variance, filtered back-projection, frequency-domain reconstruction, and time reversal. Among obtained images, common graphical features for each limiting factor were specified. Lastly, we quantitatively assessed the similarity between the image and the actual target with peak signal-to-noise ratio and structural similarity as performance indices. As a result, the characteristic features from each limiting factor were commonly identified on the reconstructed image, and the performance indices sequentially degraded following the sequence of practical conditions. ? COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.1

Functional photoacoustic imaging: from nano- and micro- to macro-scale

Abstract Functional photoacoustic imaging is a promising biological imaging technique that offers such unique benefits as scalable resolution and imaging depth, as well as the ability to provide functional information. At nanoscale, photoacoustic imaging has provided super-resolution images of the surface light absorption characteristics of materials and of single organelles in cells. At the microscopic and macroscopic scales. photoacoustic imaging techniques have precisely measured and quantified various physiological parameters, such as oxygen saturation, vessel morphology, blood flow, and the metabolic rate of oxygen, in both human and animal subjects. This comprehensive review provides an overview of functional photoacoustic imaging across multiple scales, from nano to macro, and highlights recent advances in technology developments and applications. Finally, the review surveys the future prospects of functional photoacoustic imaging in the biomedical field

New contrast agents for photoacoustic imaging and theranostics: Recent 5-year overview on phthalocyanine/naphthalocyanine-based nanoparticles

The phthalocyanine (Pc) and naphthalocyanine (Nc) nanoagents have drawn much attention as contrast agents for photoacoustic (PA) imaging due to their large extinction coefficients and long absorption wavelengths in the near-infrared region. Many investigations have been conducted to enhance Pc/Ncs' photophysical properties and address their poor solubility in an aqueous solution. Many diverse strategies have been adopted, including centric metal chelation, structure modification, and peripheral substitution. This review highlights recent advances on Pc/Nc-based PA agents and their extended use for multiplexed biomedical imaging, multimodal diagnostic imaging, and image-guided phototherapy. (C) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).11Nsciescopu

Switchable preamplifier for dual modal photoacoustic and ultrasound imaging

Photoacoustic (PA) imaging is a high-fidelity biomedical imaging technique based on the principle of molecular-specific optical absorption of biological tissue constitute. Because PA imaging shares the same basic principle as that of ultrasound (US) imaging, the use of PA/US dual-modal imaging can be achieved using a single system. However, because PA imaging is limited to a shallower depth than US imaging due to the optical extinction in biological tissue, the PA signal yields a lower signal-to-noise ratio (SNR) than US images. To selectively amplify the PA signal, we propose a switchable preamplifier for acoustic-resolution PA microscopy implemented on an application-specific integrated circuit. Using the preamplifier, we measured the increments in the SNR with both carbon lead and wire phantoms. Furthermore, in vivo whole-body PA/US imaging of a mouse with a preamplifier showed enhancement of SNR in deep tissues, unveiling deeply located organs and vascular networks. By selectively amplifying the PA signal range to a level similar to that of the US signal without contrast agent administration, our switchable amplifier strengthens the mutual complement between PA/US imaging. PA/US imaging is impending toward clinical translation, and we anticipate that this study will help mitigate the imbalance of image depth between the two imaging modalities. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.11Nsciescopu

Contrast Agent‐Free 3D Renal Ultrafast Doppler Imaging Reveals Vascular Dysfunction in Acute and Diabetic Kidney Diseases

Abstract To combat the irreversible decline in renal function associated with kidney disease, it is essential to establish non‐invasive biomarkers for assessing renal microcirculation. However, the limited resolution and/or vascular sensitivity of existing diagnostic imaging techniques hinders the visualization of complex cortical vessels. Here, a 3D renal ultrafast Doppler (UFD) imaging system that uses a high ultrasound frequency (18 MHz) and ultrahigh frame rate (1 KHz per slice) to scan the entire volume of a rat's kidney in vivo is demonstrated. The system, which can visualize the full 3D renal vascular branching pyramid at a resolution of 167 µm without any contrast agent, is used to chronically and noninvasively monitor kidneys with acute kidney injury (AKI, 3 days) and diabetic kidney disease (DKD, 8 weeks). Multiparametric UFD analyses (e.g., vessel volume occupancy (VVO), fractional moving blood volume (FMBV), vessel number density (VND), and vessel tortuosity (VT)) describe rapid vascular rarefaction from AKI and long‐term vascular degeneration from DKD, while the renal pathogeneses are validated by in vitro blood serum testing and stained histopathology. This work demonstrates the potential of 3D renal UFD to offer valuable insights into assessing kidney perfusion levels for future research in diabetes and kidney transplantation