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Diagnosing and treating skin diseases require advanced visual skills across multiple domains and the ability to synthesize information from various imaging modalities. Current deep learning models, while effective at specific tasks such as diagnosing skin cancer from dermoscopic images, fall short in addressing the complex, multimodal demands of clinical practice. Here, we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on a dataset of over 2 million real-world images of skin diseases, sourced from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse datasets covering a range of clinical tasks, including skin cancer screening, phenotype assessment and risk stratification, diagnosis of neoplastic and inflammatory skin diseases, skin lesion segmentation, change monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models even when using only 5-10% of labeled data. PanDerm's clinical utility was demonstrated through reader studies in real-world clinical settings across multiple imaging modalities. It outperformed clinicians by 10.2% in early-stage melanoma detection accuracy and enhanced clinicians' multiclass skin cancer diagnostic accuracy by 11% in a collaborative human-AI setting. Additionally, PanDerm demonstrated robust performance across diverse demographic factors, including different body locations, age groups, genders, and skin tones. The strong results in benchmark evaluations and real-world clinical scenarios suggest that PanDerm could enhance the management of skin diseases and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of AI support in healthcare., Comment: 56 pages; Technical report

Reasonable vibration reduction design is an important way to achieve low phase noise index of airborne frequency source output signal. Aiming at the problem of phase noise deterioration of an airborne frequency source under random condition, this paper proposes to improve the vibration reduction mode crystal oscillator and reduce the distance between the barycenter of frequency source and crystal oscillator vibration based on the analysis of the relationship between the frequency source and the phase noise of output signal. Experimental results show that the active noise control system achieves 62dB phase noise compensation under the random vibration of 0.04-0.1g*g/Hz amplitude range and 5-2000 Hz frequency range., Comment: There are many errors. 1.Fig. 2 Block Diagram of Frequency Source Circuit is not correct. 2.C-band C1 signal 6000MHz continuous wave signal is error. 3.Fig. 4 Steady State Phase Noise and Spectrum of 2400MHz before Improvement is error. 4.Table 1 Steady State Phase Noise at each Frequency Point of the Output of the Frequency Source before Improvement is error. 5. Frequency range is error

The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which is one of the malignant tumors with the highest mortality rate. The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming. However, there is no algorithm as yet tailored for the MVI detection from pathological images. This paper collects the first pathological liver image dataset containing 522 whole slide images with labels of vessels, MVI, and hepatocellular carcinoma grades. The first and essential step for the automatic diagnosis of MVI is the accurate segmentation of vessels. The unique characteristics of pathological liver images, such as super-large size, multi-scale vessel, and blurred vessel edges, make the accurate vessel segmentation challenging. Based on the collected dataset, we propose an Edge-competing Vessel Segmentation Network (EVS-Net), which contains a segmentation network and two edge segmentation discriminators. The segmentation network, combined with an edge-aware self-supervision mechanism, is devised to conduct vessel segmentation with limited labeled patches. Meanwhile, two discriminators are introduced to distinguish whether the segmented vessel and background contain residual features in an adversarial manner. In the training stage, two discriminators are devised tocompete for the predicted position of edges. Exhaustive experiments demonstrate that, with only limited labeled patches, EVS-Net achieves a close performance of fully supervised methods, which provides a convenient tool for the pathological liver vessel segmentation. Code is publicly available at https://github.com/zju-vipa/EVS-Net., Comment: Accepted by AAAI 2021

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal layers. Clinical researches suggest that the morphology and contour irregularity of FAZ are important biomarkers of various ocular pathologies. Therefore, precise segmentation of FAZ has great clinical interest. Also, there is no existing research reporting that FAZ features can improve the performance of deep diagnostic classification networks. In this paper, we propose a novel multi-level boundary shape and distance aware joint learning framework, named BSDA-Net, for FAZ segmentation and diagnostic classification from OCTA images. Two auxiliary branches, namely boundary heatmap regression and signed distance map reconstruction branches, are constructed in addition to the segmentation branch to improve the segmentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier. Through extensive experiments, the proposed BSDA-Net is found to yield state-of-the-art segmentation and classification results on the OCTA-500, OCTAGON, and FAZID datasets., Comment: 12 pages, 4 figures, MICCAI2021 [Student Travel Award]

Let $G$ be a finite group. The co-prime order graph of $G$ is the graph whose vertex set is $G$, and two distinct vertices $x,y$ are adjacent if gcd$(o(x),o(y))$ is either $1$ or a prime, where $o(x)$ and $o(y)$ are the orders of $x$ and $y$, respectively. In this paper, we characterize all finite groups whose co-prime order graphs are complete and classify all finite groups whose co-prime order graphs are planar. Also, we compute the vertex-connectivity of the co-prime order graph of a cyclic group, a dihedral group and a generalized quaternion group, which answers a question by Banerjee (2019). Finally, we prove that, for a fixed positive integer $k$, there are finitely many finite groups whose co-prime order graphs have (non)orientable genus $k$. As applications, we classify all finite groups whose co-prime order graphs have (non)orientable genus one and two., Comment: There is an error in Sect. 4. We can not obtain the first main result of this section