One-Stage Anchor-Free Online Multiple Target Tracking With Deformable Local Attention and Task-Aware Prediction.

The tracking-by-detection paradigm currently dominates multiple target tracking algorithms. It usually includes three tasks: target detection, appearance feature embedding, and data association. Carrying out these three tasks successively usually leads to lower tracking efficiency.

AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes.

Measurement and observation of the first web space based on morphology and clinical applications.

Purpose: To describe and evaluate the anatomical skin shape of the first web space in cadavers and to guide flap design for this area.

Methods: Twelve cadavers (24 hands on both sides) were selected. Marker points were chosen based on the characteristics of the first web for morphological measurement and observation.

Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography.

The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme. To overcome these limitations, we propose a universal, extensible framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes (e.

Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism.

Pulmonary Embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients death. This disorder is commonly diagnosed using Computed Tomography Pulmonary Angiography (CTPA). Deep learning holds great promise for the Computer-aided Diagnosis (CAD) of PE.

Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.

Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction.

Efficacy of a fingertip reconstruction technique using the tongue-shaped flap and advancement flap for repairing congenital syndactyly with osseous fusion of the distal phalanges.

Background: To explore the clinical efficacy of using tongue-shaped flaps and advancement flaps to reconstruct the fingertips in congenital syndactyly patients with osseous fusion of the distal phalanges.

Methods: From January 2016 to January 2019, 12 patients with congenital syndactyly, involving 30 digits in total, presented to our hospital and were surgically treated with tongue-shaped flaps, as well as with advancement flaps to reconstruct the fingertips. The flap infection rate, necrosis rate and any other early complications were recorded.

Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection.

Pulmonary embolism (PE) represents a thrombus ("blood clot"), usually originating from a lower extremity vein, that travels to the blood vessels in the lung, causing vascular obstruction and in some patients, death. This disorder is commonly diagnosed using CT pulmonary angiography (CTPA). Deep learning holds great promise for the computer-aided CTPA diagnosis (CAD) of PE.

Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious, laborious, and time consuming, but it also demands costly, specialty-oriented skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning.

Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-Supervised Learning.

This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imaging-focusing on particular parts of the body for defined clinical purposes-generates images of great similarity in anatomy across patients and yields sophisticated anatomical patterns across images, which are associated with rich semantics about human anatomy and which are natural visual words. We show that these visual words can be automatically harvested according to anatomical consistency via self-discovery, and that the self-discovered visual words can serve as strong yet free supervision signals for deep models to learn semantics-enriched generic image representation via self-supervision (self-classification and self-restoration).

Models Genesis.

Transfer learning from natural image to medical image has been established as one of the most practical paradigms in deep learning for medical image analysis. To fit this paradigm, however, 3D imaging tasks in the most prominent imaging modalities (e.g.

Parts2Whole: Self-supervised Contrastive Learning via Reconstruction.

Contrastive representation learning is the state of the art in computer vision, but requires huge mini-batch sizes, special network design, or memory banks, making it unappealing for 3D medical imaging, while in 3D medical imaging, reconstruction-based self-supervised learning reaches a new height in performance, but lacks mechanisms to learn contrastive representation; therefore, this paper proposes a new framework for self-supervised contrastive learning via reconstruction, called Parts2Whole, because it exploits the and part-whole relationship to learn contrastive representation without using contrastive loss: Reconstructing an image (whole) from its own parts compels the model to learn similar latent features for all its own parts, while reconstructing different images (wholes) from their respective parts forces the model to simultaneously push those parts belonging to different wholes farther apart from each other in the latent space; thereby the trained model is capable of distinguishing images. We have evaluated our Parts2Whole on five distinct imaging tasks covering both classification and segmentation, and compared it with four competing publicly available 3D pretrained models, showing that Parts2Whole significantly outperforms in two out of five tasks while achieves competitive performance on the rest three. This superior performance is attributable to the contrastive representations learned with Parts2Whole.

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration.

Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster deep semantic representation learning and yield semantically more powerful models for different medical applications. But how exactly such strong yet free semantics embedded in medical images can be harnessed for self-supervised learning remains largely unexplored. To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis.

Transfer learning from image to image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (, CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created (with no manual labeling), self-taught (learned by self-supervision), and generic (served as source models for generating application-specific target models).

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization.

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation, an ability that we call fixed-point translation, which no GAN is equipped with yet. Therefore, we propose a new GAN, called Fixed-Point GAN, trained by (1) supervising same-domain translation through a conditional identity loss, and (2) regularizing cross-domain translation through revised adversarial, domain classification, and cycle consistency loss.

[Clinical practice guidelines for brachydactyly type A1].

Brachydactyly type A1 (BDA1) is the first autosomal dominant genetic disease recorded in the literature. The main characteristics of BDA1 include shortening of the middle phalanx and fusion of the middle and distal phalanges. So far more than 100 pedigrees have been reported around the world.

Ginkgolide B improves multiterritory perforator flap survival by inhibiting endoplasmic reticulum stress and oxidative stress.

The therapeutics used to promote perforator flap survival function induces vascular regeneration and inhibit apoptosis. The present study aimed to explore the potential mechanism of the angiogenesis effects of Ginkgolide B (GB) in perforator flaps. A total of 72 rats were divided into three groups and treated with saline, GB, or GB + tunicamycin (TM; ER stress activator) for seven consecutive days, respectively.

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation.

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++.

The Effect of Leonurine on Multiterritory Perforator Flap Survival in Rats.

Background: Leonurine (Leo), a natural active compound of Leonurus cardiaca, has been shown to possess various biological activities. However, it is not known whether Leo promotes perforator flap survival.

Methods: In this study, a perforator flap was outlined in the rat dorsum.

Nail size and appearance following nail fusion plasty of thumb duplication.

Purpose: To evaluate nail appearance after nail fusion plasty to treat thumb duplication.

Methods: A modified form of nail fusion plasty was performed on 17 reconstructed thumbs of 16 children with thumb duplications, commencing in January 2010. We assessed nail width and nail, lunular, and nail fold deformities using the Wang-Gao scoring system.

Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation.

Cardiovascular disease (CVD) is the number one killer in the USA, yet it is largely preventable (World Health Organization 2011). To prevent CVD, carotid intima-media thickness (CIMT) imaging, a noninvasive ultrasonography method, has proven to be clinically valuable in identifying at-risk persons before adverse events. Researchers are developing systems to automate CIMT video interpretation based on deep learning, but such efforts are impeded by the lack of large annotated CIMT video datasets.

UNet++: A Nested U-Net Architecture for Medical Image Segmentation.

In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks.

[Recent progress in research of congenital polydactyly].

Surgery is still the main treatment for congenital polydactyly, and the aim of surgical reconstruction is to obtain a thumb with excellent function and appearance. A systematic assessment of polydactyly is required prior to surgery, including bone stress lines, joint deviation, joint activity and joint instability, size and development of finger and nail. Bone shape, joint incongruency, and abnormal tendon insertions must be corrected completely, in order to obtain good function and to avoide secondary surgery.

Aloperine induces apoptosis and inhibits invasion in MG-63 and U2OS human osteosarcoma cells.

Aloperine (ALO) is a novel type of alkaloid drug that is extracted from S. alopecuroide, and exert an anti-inflammatory, anti-allergenic, antitumor and antiviral effects. In our study, we evaluated the effects and underlying mechanisms of ALO on MG-63 and U2OS osteosarcoma (OS) cells.

Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally.

Intense interest in applying convolutional neural networks (CNNs) in biomedical image analysis is wide spread, but its success is impeded by the lack of large annotated datasets in biomedical imaging. Annotating biomedical images is not only tedious and time consuming, but also demanding of costly, specialty-oriented knowledge and skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method called AIFT (active, incremental fine-tuning) to naturally integrate active learning and transfer learning into a single framework.