Optimizing the thermal performance of building envelopes for energy saving in underground office buildings in various climates of China

This article investigates the influence of the thermal performance of building envelopes on annual energy consumption in a ground-buried office building by means of the dynamic building energy simulation, aiming at offering reasonable guidelines for the energy efficient design of envelopes for underground office buildings in China. In this study, the accuracy of dealing with the thermal process for underground buildings by using the Designer's Energy Simulation Tool (DeST) is validated by measured data. The analyzed results show that the annual energy consumptions for this type of buildings vary significantly, and it is based on the value of the overall heat transfer coefficient (U-value) of the envelopes. Thus, it is necessary to optimize the U-value for underground buildings located in various climatic zones in China. With respect to the roof, an improvement in its thermal performance is significantly beneficial to the underground office building in terms of annual energy demand. With respect to the external walls, the optimized U-values completely change with the distribution of the climate zones. The recommended optimal values for various climate zones of China are also specified as design references for public office building in underground in terms of the building energy efficiency

Learning Robust Medical Image Segmentation from Multi-source Annotations

Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images. In this paper, we propose an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which guides the training process by uncertainty estimation at both the pixel and the image levels. First, we developed the annotation uncertainty estimation module (AUEM) to learn the pixel-wise uncertainty of each annotation, which then guided the network to learn from reliable pixels by weighted segmentation loss. Second, a quality assessment module (QAM) was proposed to assess the image-level quality of the input samples based on the former assessed annotation uncertainties. Importantly, we introduced an auxiliary predictor to learn from the low-quality samples instead of discarding them, which ensured the preservation of their representation knowledge in the backbone without directly accumulating errors within the primary predictor. Extensive experiments demonstrated the effectiveness and feasibility of our proposed UMA-Net on various datasets, including 2D chest X-ray segmentation, fundus image segmentation, and 3D breast DCE-MRI segmentation

Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images

Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to be delineated precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this paper, we present a scale-aware super-resolution network to adaptively segment lesions of various sizes from the low-resolution medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask super-resolution and lesion image super-resolution. The image super-resolution branch will provide more detailed features for the segmentation branch, i.e., the mask super-resolution branch, for fine-grained segmentation. Meanwhile, we introduce scale-aware dilated convolution blocks into the multi-task decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared to other state-of-the-art methods.Comment: Journal paper under review. 10 pages. The first two authors contributed equall

Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images

Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in preventing mortality and improving patient outcome. Normally, developing DL-based object detection models requires huge amount of bounding box annotation. However, annotating medical data is time-consuming and expertise-demanding, making obtaining a large amount of fine-grained annotations extremely infeasible. This poses pressing need of developing label-efficient detection models to alleviate radiologists' labeling burden. To tackle this challenge, the literature of object detection has witnessed an increase of weakly-supervised and semi-supervised approaches, yet still lacks a unified framework that leverages various forms of fully-labeled, weakly-labeled, and unlabeled data. In this paper, we present a novel omni-supervised object detection network, ORF-Netv2, to leverage as much available supervision as possible. Specifically, a multi-branch omni-supervised detection head is introduced with each branch trained with a specific type of supervision. A co-training-based dynamic label assignment strategy is then proposed to enable flexibly and robustly learning from the weakly-labeled and unlabeled data. Extensively evaluation was conducted for the proposed framework with three rib fracture datasets on both chest CT and X-ray. By leveraging all forms of supervision, ORF-Netv2 achieves mAPs of 34.7, 44.7, and 19.4 on the three datasets, respectively, surpassing the baseline detector which uses only box annotations by mAP gains of 3.8, 4.8, and 5.0, respectively. Furthermore, ORF-Netv2 consistently outperforms other competitive label-efficient methods over various scenarios, showing a promising framework for label-efficient fracture detection.Comment: 11 pages, 4 figures, and 7 table

Two-dimensional Massless Dirac Fermions in Antiferromagnetic AFe2As2 (A = Ba, Sr)

We report infrared studies of AFe$_{2}$As$_{2}$ (A = Ba, Sr), two representative parent compounds of iron-arsenide superconductors, at magnetic fields (B) up to 17.5 T. Optical transitions between Landau levels (LLs) were observed in the antiferromagnetic states of these two parent compounds. Our observation of a $\sqrt{B}$ dependence of the LL transition energies, the zero-energy intercepts at B = 0 T under the linear extrapolations of the transition energies and the energy ratio ($\sim$ 2.4) between the observed LL transitions, combined with the linear band dispersions in two-dimensional (2D) momentum space obtained by theoretical calculations, demonstrates the existence of massless Dirac fermions in antiferromagnetic BaFe$_{2}$As$_{2}$. More importantly, the observed dominance of the zeroth-LL-related absorption features and the calculated bands with extremely weak dispersions along the momentum direction $k_{z}$ indicate that massless Dirac fermions in BaFe$_{2}$As$_{2}$ are 2D. Furthermore, we find that the total substitution of the barium atoms in BaFe$_{2}$As$_{2}$ by strontium atoms not only maintains 2D massless Dirac fermions in this system, but also enhances their Fermi velocity, which supports that the Dirac points in iron-arsenide parent compounds are topologically protected.Comment: Magneto-infrared study, Landau level spectroscopy, DFT+DMFT calculation

Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification

Deep learning models were frequently reported to learn from shortcuts like dataset biases. As deep learning is playing an increasingly important role in the modern healthcare system, it is of great need to combat shortcut learning in medical data as well as develop unbiased and trustworthy models. In this paper, we study the problem of developing debiased chest X-ray diagnosis models from the biased training data without knowing exactly the bias labels. We start with the observations that the imbalance of bias distribution is one of the key reasons causing shortcut learning, and the dataset biases are preferred by the model if they were easier to be learned than the intended features. Based on these observations, we proposed a novel algorithm, pseudo bias-balanced learning, which first captures and predicts per-sample bias labels via generalized cross entropy loss and then trains a debiased model using pseudo bias labels and bias-balanced softmax function. We constructed several chest X-ray datasets with various dataset bias situations and demonstrated with extensive experiments that our proposed method achieved consistent improvements over other state-of-the-art approaches.Comment: To appear in MICCAI 2022. Code available at https://github.com/LLYXC/PBB