Deep metric attention learning for skin lesion classification in dermoscopy images

Currently, convolutional neural networks (CNNs) have made remarkable achievements in skin lesion classification because of their end-to-end feature representation abilities. However, precise skin lesion classification is still challenging because of the following three issues: (1) insufficient training samples, (2) inter-class similarities and intra-class variations, and (3) lack of the ability to focus on discriminative skin lesion parts. To address these issues, we propose a deep metric attention learning CNN (DeMAL-CNN) for skin lesion classification. In DeMAL-CNN, a triplet-based network (TPN) is first designed based on deep metric learning, which consists of three weight-shared embedding extraction networks. TPN adopts a triplet of samples as input and uses the triplet loss to optimize the embeddings, which can not only increase the number of training samples, but also learn the embeddings robust to inter-class similarities and intra-class variations. In addition, a mixed attention mechanism considering both the spatial-wise and channel-wise attention information is designed and integrated into the construction of each embedding extraction network, which can further strengthen the skin lesion localization ability of DeMAL-CNN. After extracting the embeddings, three weight-shared classification layers are used to generate the final predictions. In the training procedure, we combine the triplet loss with the classification loss as a hybrid loss to train DeMAL-CNN. We compare DeMAL-CNN with the baseline method, attention methods, advanced challenge methods, and state-of-the-art skin lesion classification methods on the ISIC 2016 and ISIC 2017 datasets, and test its generalization ability on the PH2 dataset. The results demonstrate its effectiveness.

High Precision Mammography Lesion Identification From Imprecise Medical Annotations

Breast cancer screening using Mammography serves as the earliest defense against breast cancer, revealing anomalous tissue years before it can be detected through physical screening. Despite the use of high resolution radiography, the presence of densely overlapping patterns challenges the consistency of human-driven diagnosis and drives interest in leveraging state-of-art localization ability of deep convolutional neural networks (DCNN). The growing availability of digitized clinical archives enables the training of deep segmentation models, but training using the most widely available form of coarse hand-drawn annotations works against learning the precise boundary of cancerous tissue in evaluation, while producing results that are more aligned with the annotations rather than the underlying lesions. The expense of collecting high quality pixel-level data in the field of medical science makes this even more difficult. To surmount this fundamental challenge, we propose LatentCADx, a deep learning segmentation model capable of precisely annotating cancer lesions underlying hand-drawn annotations, which we procedurally obtain using joint classification training and a strict segmentation penalty. We demonstrate the capability of LatentCADx on a publicly available dataset of 2,620 Mammogram case files, where LatentCADx obtains classification ROC of 0.97, AP of 0.87, and segmentation AP of 0.75 (IOU = 0.5), giving comparable or better performance than other models. Qualitative and precision evaluation of LatentCADx annotations on validation samples reveals that LatentCADx increases the specificity of segmentations beyond that of existing models trained on hand-drawn annotations, with pixel level specificity reaching a staggering value of 0.90. It also obtains sharp boundary around lesions unlike other methods, reducing the confused pixels in the output by more than 60%.

Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems

Multimodal optimization problem exists in multiple global and many local optimal solutions. The difficulty of solving these problems is finding as many local optimal peaks as possible on the premise of ensuring global optimal precision. This article presents adaptive grouping brainstorm optimization (AGBSO) for solving these problems. In this article, adaptive grouping strategy is proposed for achieving adaptive grouping without providing any prior knowledge by users. For enhancing the diversity and accuracy of the optimal algorithm, elite reservation strategy is proposed to put central particles into an elite pool, and peak detection strategy is proposed to delete particles far from optimal peaks in the elite pool. Finally, this article uses testing functions with different dimensions to compare the convergence, accuracy, and diversity of AGBSO with BSO. Experiments verify that AGBSO has great localization ability for local optimal solutions while ensuring the accuracy of the global optimal solutions.

Investigation of an MAA Test With Virtual Sound Synthesis

The ability to localize a sound source is very important in our daily life, specifically to analyze auditory scenes in complex acoustic environments. The concept of minimum audible angle (MAA), which is defined as the smallest detectable difference between the incident directions of two sound sources, has been widely used in the research fields of auditory perception to measure localization ability. Measuring MAAs usually involves a reference sound source and either a large number of loudspeakers or a movable sound source in order to reproduce sound sources at a large number of predefined incident directions. However, existing MAA test systems are often cumbersome because they require a large number of loudspeakers or a mechanical rail slide and thus are expensive and inconvenient to use. This study investigates a novel MAA test method using virtual sound source synthesis and avoiding the problems with traditional methods. We compare the perceptual localization acuity of sound sources in two experimental designs: using the virtual presentation and real sound sources. The virtual sound source is reproduced through a pair of loudspeakers weighted by vector-based amplitude panning (VBAP). Results show that the average measured MAA at 0° azimuth is 1.1° and the average measured MAA at 90° azimuth is 3.1° in a virtual acoustic system, meanwhile the average measured MAA at 0° azimuth is about 1.2° and the average measured MAA at 90° azimuth is 3.3° when using the real sound sources. The measurements of the two methods have no significant difference. We conclude that the proposed MAA test system is a suitable alternative to more complicated and expensive setups.

Benefits of Binaural Integration in Cochlear Implant Patients with Single-Sided Deafness and Residual Hearing in the Implanted Ear

The purpose of the study is to gauge the benefits of binaural integration effects (redundancy and squelch) due to preserved low-frequency residual hearing in the implanted ear of cochlear implant users with single-sided deafness. There were 11 cochlear implant users (age 18–61 years old) who had preserved low-frequency hearing in the implanted ear; they had a normal hearing or mild hearing loss in the contralateral ear. Patients were tested with monosyllabic words, under different spatial locations of speech and noise and with the cochlear implant activated and deactivated, in two listening configurations—one in which low frequencies in the implanted ear were masked and another in which they were unmasked. We also investigated how cochlear implant benefit due to binaural integration depended on unaided sound localization ability. Patients benefited from the binaural integration effects of redundancy and squelch only in the unmasked condition. Pearson correlations between binaural integration effects and unaided sound localization error showed significance only for squelch (r = −0.67; p = 0.02). Hearing preservation after cochlear implantation has considerable benefits because the preserved low-frequency hearing in the implanted ear contributes to binaural integration, presumably through the preserved temporal fine structure.

A static sound source can improve postural stability during walking

BACKGROUND: During walking, postural stability is controlled by visual, vestibular and proprioceptive input. The auditory system uses acoustic input to localize sound sources. For some static balance conditions, the auditory influence on posture was already proven. Little is known about the impact of auditory inputs on balance in dynamic conditions. OBJECTIVE: This study is aimed at investigating postural stability of walking tasks in silence and sound on condition to better understand the impact of auditory input on balance in movement. METHODS: Thirty participants performed: walking (eyes open), tandem steps, walking with turning head and walking over barriers. During each task, acoustic condition changed between silence and presented noise through an earth-fixed loudspeaker located at the end of the walking distance. Body sway velocity was recorded close to the body’s center of gravity. RESULTS: A decreased body sway velocity was significant for walking (eyes open), tandem steps and walking over barriers when noise was presented. Those auditory stimuli did not affect sway velocity while walking with turning head. The posture has probably improved due to the localization ability when walking with the head facing forward, while the localization ability was impaired when turning the head. CONCLUSIONS: The localization ability of a fixed sound source through the auditory system has a significant but limited impact on posture while walking.

Wireless Localization Based on Deep Learning: State of Art and Challenges

The problem of position estimation has always been widely discussed in the field of wireless communication. In recent years, deep learning technology is rapidly developing and attracting numerous applications. The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models. Consequently, wireless localization based on deep learning has attracted extensive research during the last decade. The research and applications on wireless localization technology based on deep learning are reviewed in this paper. Typical deep learning models are summarized with emphasis on their inputs, outputs, and localization methods. Technical details helpful for enhancing localization ability are also mentioned. Finally, some problems worth further research are discussed.