Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation

Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medical image segmentation models without access to the source domain. Ensuring consistency between target edges and paired inputs is crucial for test-time adaptation. To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation. Specifically, we introduce a local boundary consistency constraint method that explores the relationship between tissue region segmentation and tissue boundary localization tasks. Additionally, we propose a global feature consistency constraint toto enhance the intra-class compactness. We conduct extensive experiments on the segmentation of benchmark fundus images. Compared to prediction directly by the source domain model, the segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and Drishti GS datasets, respectively. Additionally, the results of experiments demonstrate that our proposed method outperforms existing competitive domain adaptation segmentation algorithms.Comment: 31 pages,7 figure

Attention Guided 3D U-Net for KiTS19

We use a two-stage 3d U-Net model to predict the multi channels segmentations from coarse to fine. The second stage is guided by the predictions from the first stage. 1 Method We proposed a two stages method to segment CT image from coarse to fine. The two stages are trained with different learning scope and are assigned with different learning missions. 1.1 Stage 1 – Coarse stage Data preprocess. Firstly, we downscale the training data to a normal shape, in order to make sure the model can take a whole image at once. All the images and segmentations are downscale to 128*128*32 (height*width*depth). The segmentation files are transformed to 3-channels arrays, in which the channels-wise pixel values represent kidneys, tumors and the background (without kidneys and tumors) in order. Training. We train the standard 3D U-Net follow with a softmax layer. While training, we apply some data augmentation to the training data, including normalize, random contrast, random flip and random rotate. We input all the 210 cases training data and train the model to regress the multi-channel segmentations. We apply with pytorch, and the learning rate is 0.1 which divide 0.1 in 300000 epochs and 500000 epochs. We use the Binary Cross Entropy Loss as loss function. Predicting. The 90 cases testing images are preprocessed the same with the training images then input to the trained model. The channel-wise predictions are scaled back the original shape. We take the first 2 channels of the predictions, represent as the segmentation of kidneys and tumors, then package as the .nii.gz files

Relation-aware Ensemble Learning for Knowledge Graph Embedding

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.Comment: This short paper has been accepted by EMNLP 202

Investigation of the microcrack evolution in a Ti-based bulk metallic glass matrix composite

AbstractThe initiation and evolution behavior of the shear-bands and microcracks in a Ti-based metallic-glass–matrix composite (MGMC) were investigated by using an in-situ tensile test under transmission electron microscopy (TEM). It was found that the plastic deformation of the Ti-based MGMC related with the generation of the plastic deformation zone in crystalline and shear deformation zone in glass phase near the crack tip. The dendrites can suppress the propagation of the shear band effectively. Before the rapid propagation of cracks, the extending of plastic deformation zone and shear deformation zone ahead of crack tip is the main pattern in the composite

Dust Episodes in Hong Kong (South China) and their Relationship with the Sharav and Mongolian Cyclones and Jet Streams

The study presented in this paper analyses two dust episodes in Hong Kong, one occurring in March 2006 and the other on 22 March 2010. The latter is the worst dust episode on Hong Kong record. The focus is on the relationship between the dust episodes and the Sharav/Mongolian cyclones and jet streams. The 16 March 2006 episode is traceable to a continental-scale Saharan dust outbreak of 5-9 March 2006 caused by the cold front of an East Mediterranean Sharav cyclone arriving at north-west Africa on 5 March 2006. The eastward movement of the cyclone along the North African coast is clearly illustrated in the geopotential height contours. Simulations by the chemistry transport model GOCART provide a visible evidence of the transport as well as an estimate of contributions from the Sahara to the aerosol concentration levels in Hong Kong. The transport simulations suggest that the dust is injected to the polar jet north of the Caspian Sea, while it is transported eastward simultaneously by the more southerly subtropical jet. The major source of dust for Hong Kong is usually the Gobi desert. Despite the effect of remote sources, the 16 March 2006 dust episode was still mainly under the influence of the Mongolian cyclone cold fronts. In the recent episode of 22 March 2010, the influence of the Mongolian cyclone predominated as well. It appears that the concurrent influence of the Sharav and Mongolian cyclones on Hong Kong and East Asia is not a common occurrence. Besides transporting dusts from non-East Asian sources to Hong Kong and East Asia, the strong subtropical jet on 21 March 2010 (i.e. 1 day prior to the major dust episode) is believed to have strengthened an easterly monsoon surge to South China causing the transport of voluminous dusts to Taiwan and Hong Kong the following day

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography

Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by cryo-ET are difficult due to high structural complexity and imaging limits. Deep learning based subtomogram classification have played critical roles for such tasks. As supervised approaches, however, their performance relies on sufficient and laborious annotation on a large training dataset. Results: To alleviate this major labeling burden, we proposed a Hybrid Active Learning (HAL) framework for querying subtomograms for labelling from a large unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select the subtomograms that have the most uncertain predictions. Moreover, to mitigate the sampling bias caused by such strategy, a discriminator is introduced to judge if a certain subtomogram is labeled or unlabeled and subsequently the model queries the subtomogram that have higher probabilities to be unlabeled. Additionally, HAL introduces a subset sampling strategy to improve the diversity of the query set, so that the information overlap is decreased between the queried batches and the algorithmic efficiency is improved. Our experiments on subtomogram classification tasks using both simulated and real data demonstrate that we can achieve comparable testing performance (on average only 3% accuracy drop) by using less than 30% of the labeled subtomograms, which shows a very promising result for subtomogram classification task with limited labeling resources.Comment: Statement on authorship changes: Dr. Eric Xing was an academic advisor of Mr. Haohan Wang. Dr. Xing was not directly involved in this work and has no direct interaction or collaboration with any other authors on this work. Therefore, Dr. Xing is removed from the author list according to his request. Mr. Zhenxi Zhu's affiliation is updated to his current affiliatio