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The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on translating radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest computed tomography lung cancer screening scans and 76 brain magnetic resonance imaging metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.27 in the five-point system with 0.08 places of information missing and 0.07 places of misinformation. In terms of the suggestions provided by ChatGPT, they are generally relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.

The similarity among samples and the discrepancy between clusters are two crucial aspects of image clustering. However, current deep clustering methods suffer from the inaccurate estimation of either feature similarity or semantic discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy. We design two semantics-aware pseudo-labeling algorithms, prototype pseudo-labeling, and reliable pseudo-labeling, which enable accurate and reliable self-supervision over clustering. Without using any ground-truth label, we optimize the clustering network in three stages: 1) train the feature model through contrastive learning to measure the instance similarity, 2) train the clustering head with the prototype pseudo-labeling algorithm to identify cluster semantics, and 3) jointly train the feature model and clustering head with the reliable pseudo-labeling algorithm to improve the clustering performance. Extensive experimental results demonstrate that SPICE achieves significant improvements (~10%) over existing methods and establishes the new state-of-the-art clustering results on six image benchmark datasets in terms of three popular metrics. Importantly, SPICE significantly reduces the gap between unsupervised and fully-supervised classification; e.g., there is only a 2% (91.8% vs 93.8%) accuracy difference on CIFAR-10. Our code has been made publically available at https://github.com/niuchuangnn/SPICE.

Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from ultra-sparse projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed by the image domain networks. After that, the refinement module focuses on the recovery of image details in the residual data and image domains synergistically. Finally, the results from embedding and refinement components in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.

A novel and efficient palladium-catalyzed C-H activation reaction of [60]fullerene with arylphosphinic acids has been developed for the synthesis of [60]fullerene-fused phosphinolactones. A possible reaction mechanism is proposed to explain the generation of the obtained products. A representative product can be further electrochemically transformed into bis-benzylated 1,2- and 1,4-adducts of [60]fullerene. In addition, a [60]fullerene-fused phosphinolactone with a 12-membered ring can also be synthesized from the electrochemical ring expansion of the employed phosphinolactone with a 6-memebered ring with 1,2-bis(bromomethyl)benzene.

The electrosynthesis of decorated basket molecules, that is, [60]fullerene-fused 12-membered macrolactones, has been achieved efficiently for the first time by the electrochemical reduction of [60]fullerene-fused 6-membered lactones and subsequent ring expansion with 1,2-bis(1-bromoalkyl)benzenes. The observed isomeric distributions of the obtained macrolactones are elucidated by theoretical calculations. The product structures have been firmly established by single-crystal X-ray analyses.

A novel and efficient Cu(I)-catalyzed radical heteroannulation reaction of [60]fullerene (C60) with α-bromo acetamides has been disclosed for the direct synthesis of diverse C60-fused lactams. Furthermore, the formed C60-fused lactams can be served as a versatile platform for further electrochemical functionalization to prepare 1,2-, 1,4-, 1,2,3,16-, and 1,4,9,25-adducts of C60. In addition, a representative fullerene product has been applied as an overcoating layer of the electron-transporting layer in n-type perovskite solar cell.

Endohedral clusterfullerenes exhibit unique chemical properties due to intramolecular electron transfer of the encaged metal cluster to the outer fullerene cages. Herein, we report the synthesis of two Sc 3 N@ D 3 h -C 78 monoadducts 2a and 2b through the 1, 3-dipolar reaction of Sc 3 N@ D 3 h -C 78 with carbonyl ylide bearing anomalous cis-conformation regioselectivity. The molecular structures of these monoadducts are unambiguously confirmed by single-crystal X-ray crystallography, revealing that both of 2a and 2b have cis-conformations within furan moiety grafted via [6,6]-closed addition patterns. Under the same conditions, the control reaction of C 60 with carbonyl ylide affords two monoadducts 3a and 3b , which however exhibit respectively the cis - and trans -conformations with [6,6]-closed addition patterns. According to theoretical calculations, the exclusive formation of the cis -only Sc 3 N@ D 3 h -C 78 monoadducts is interpreted by the conjunct effects of thermodynamic stability of adducts, the reactivity of the addition site, and the cis-dipole intermediate from trans 1 .

The multicomponent cascade reaction of Sc

Benzylation of the electrochemically generated dianion from N-p-tolyl-[60]fullerooxazolidinone with benzyl bromide provides three products with different addition patterns. The product distribution can be dramatically altered by varying the reaction conditions. Based on spectral characterizations, these products have been assigned as mono-benzylated 1,4-adduct and bis-benzylated 1,2,3,16- and 1,4,9,25-adducts, respectively. The assigned 1,2,3,16-adduct has been further established by X-ray diffraction analysis. It is believed that the 1,4-adduct is obtained by decarboxylative benzylation of the dianionic species, while bis-benzylated 1,2,3,16- and 1,4,9,25-adducts are achieved via a rearrangement process. In addition, the electrochemical properties of these products have been studied.

While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary example. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and technologies of micro-focus X-ray source, photon-counting detector (PCD), and robotic arms for ultrahigh resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic-arm based micro-CT scanner for a local scan at much higher spatial and spectral resolution as well as much reduced radiation dose. The prior information from global scan is also fully utilized for background compensation to improve interior tomography from local data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers superior local reconstruction, being insensitive to the misalignment of the isocenter position and initial view angle in the data/image registration while the attenuation error caused by scale mismatch can be effectively addressed with bias correction. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With high resolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great potentials in temporal bone imaging as well as various other clinical applications., Comment: 10 pages, 13 figures, 3 tables

Electrochemical alkylations of a [60]fulleroindoline with different bulky alkyl bromides exhibit different reaction behaviors. The hydroalkylation and dialkylation of the electrochemically generated dianionic [60]fulleroindoline with bulky 2,4,6-tris(bromomethyl)mesitylene give rise to 1,2,3,16-adducts. In comparison, the hydroalkylation of the dianionic [60]fulleroindoline with bulkier diphenylbromomethane still affords a 1,2,3,16-adduct, while the corresponding dialkylation provides a sterically favoured 1,4,9,12-adduct, which is scarcely investigated, as the major product along with the isomeric 1,2,3,16-adduct as the minor product. The structures of these products have been determined by spectroscopic data and single-crystal X-ray diffraction analysis. A plausible reaction mechanism has been proposed to explain the formation of the observed products.

An efficient synthesis of [60]fullerene-fused furochromenones has been developed by the palladium-catalyzed heteroannulation of [60]fullerene with 4-hydroxycoumarins. The formed [60]fullerene-fused furochromenones can be further derivatized via an electrochemical method to synthesize 1,2,3,4-adducts in high yields.

The conversion of the [60]fullerene-fused lactones to ketones with triflic anhydride as an unusual reductant under aerobic conditions has been achieved in excellent yields. The present thermal retro Baeyer-Villiger reaction from lactones to ketones has no precedents. In addition, the unique ring-opened peroxy [60]fullerene derivatives can be obtained by the electrochemical reactions of the synthesized [60]fullerene-fused ketones.

Objective. Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. However, accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to not only the variability in size, location, and appearance of lung nodules but also the complexity of lung structures. This leads to a high false-positive rate with CAD, compromising its clinical efficacy. Approach. Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules among a set of candidate regions. Specifically, a 3D vision transformer is developed that divides a CT volume into a sequence of non-overlap cubes, extracts embedding features from each cube with an embedding layer, and analyzes all embedding features with a self-attention mechanism for the prediction. To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images. Results. Our experiments show that the proposed method can significantly improve the performance of lung nodule screening in comparison with the commonly used 3D convolutional neural networks. Significance. This study demonstrates a promising direction to improve the performance of current CAD systems for lung nodule detection.

Electrochemical benzylation of [60]fullerene-fused lactones with different motifs unexpectedly affords three types of ring-opened benzylated adducts under different conditions. Both [60]fullerene-fused δ- and γ-lactones can be benzylated to generate ring-opened mono- and bis-benzylated adducts in good yields. In addition, representative fullerene products have been applied as efficient electron-transport materials in perovskite solar cells.

The width of auroral arc is one of the most important factors in understanding and examining its physical mechanisms. In this letter, we propose a fully automatic method for computing the width of auroral arcs based on the instance segmentation of auroral images. To accurately detect and segment auroral arcs with oriented bounding boxes, we adapt a state-of-the-art instance segmentation model, Mask region-based convolutional neural network, by designing a two-stage inference process combined with an indispensable random rotation training strategy and designing an effective feature extraction architecture. Given the segmented masks of individual auroral arcs, we present a method for computing the arc width automatically. In our experiments, the instance segmentation model achieves 86.8% of mean average precision on the human-labeled data set. By automatically evaluating the width of 29 938 detected auroral arcs in 18 417 auroral arc images, we obtain a similar arc width distribution to that evaluated by the semiautomatic approach, which demonstrates the effectiveness of our proposed method.

The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, which can adversely affect not only the radiologists judgement but also the performance of downstream medical image analysis tasks. Various low-dose CT denoising methods, especially the recent deep learning based approaches, have produced impressive results. However, the existing denoising methods are all downstream-task-agnostic and neglect the diverse needs of the downstream applications. In this paper, we introduce a novel Task-Oriented Denoising Network (TOD-Net) with a task-oriented loss leveraging knowledge from the downstream tasks. Comprehensive empirical analysis shows that the task-oriented loss complements other task-agnostic losses by steering the denoiser to enhance the image quality in the task related regions of interest. Such enhancement in turn brings general boosts on the performance of various methods for the downstream task. The presented work may shed light on the future development of context-aware image denoising methods. Code is available at https://github.com/DIAL-RPI/Task-Oriented-CT-Denoising_TOD-Net.

Due to lack of the kernel awareness, some popular deep image reconstruction networks are unstable. To address this problem, here we introduce the bounded relative error norm (BREN) property, which is a special case of the Lipschitz continuity. Then, we perform a convergence study consisting of two parts: (1) a heuristic analysis on the convergence of the analytic compressed iterative deep (ACID) scheme (with the simplification that the CS module achieves a perfect sparsification), and (2) a mathematically denser analysis (with the two approximations: [1] A

Solvent-free mechanical milling is a new, environmentally friendly and cost-effective technology that is now widely used in the field of organic synthesis. The mechanochemical solvent-free synthesis of furoxans from aldoximes was achieved through dimerization of the in situ generated nitrile oxides in the presence of sodium chloride, Oxone and a base. A variety of furoxans was obtained with up to a 92% yield. The present protocol has the advantages of high reaction efficiency and mild reaction conditions.

Endohedral clusterfullerenes exhibit unique chemical properties due to intramolecular electron transfer of the encaged metal cluster to the outer fullerene cages. We report the synthesis of two Sc

An efficient one-pot three-component domino coupling reaction of phenols, C60, and bromoalkanes was developed, resulting in the highly regioselective synthesis of 1,4-asymmetrical C60 bisadducts. The reaction utilizes KOtBu as a promoter and likely proceeds by an oxyanion/carbanion rearrangement/nucleophilic addition cascade. This new methodology is particularly effective for the synthesis of 1,4-asymmetrical C60 electron transport materials. Its utility is demonstrated by the synthesis of a new efficient 1,4-C60 ETM, which possesses better performance, easier synthesis, and a lower cost compared with the commercially available PCBM.

The solvent-free mechanochemical reaction has aroused increasing interest among scientists. Mechanical ball-milling can implement reactions under mild conditions, shorten the reaction time, and improve the reaction efficiency. Particularly, the most attractive characteristic of mechanochemistry is that it can alter the reaction pathway. However, few such examples have been reported so far. In this paper, we report the reaction of aldoximes with NaCl and Oxone under ball-milling conditions to afford N-acyloxyimidoyl chlorides, which are different from those of the liquid-phase counterpart.

A series of cyclopentafullerenes have been synthesized in high stereoselectivity by the thermal reaction of [60]fullerene with aldehydes and secondary amines. Both α,β-unsaturated aldehydes and saturated aldehydes can be utilized to synthesize cyclopentafullerenes as the cis isomers. The possible reaction mechanisms for the formation of cyclopentafullerenes are proposed on the basis of the experimental results.

With the recent advance in chemical modification of fullerenes, electrosynthesis has demonstrated increasing importance in regioselective synthesis of novel fullerene derivatives. Herein, we report successively regioselective synthesis of stable tetra- and hexafunctionalized [60]fullerene derivatives. The cycloaddition reaction of the electrochemically generated dianions from [60]fulleroindolines with phthaloyl chloride regioselectively affords 1,2,4,17-functionalized [60]fullerene derivatives with two attached ketone groups and a unique addition pattern, where the heterocycle is rearranged to a [5,6]-junction and the carbocycle is fused to an adjacent [6,6]-junction. This addition pattern is in sharp contrast with that of the previously reported biscycloadducts, where both cycles are appended to [6,6]-junctions. The obtained tetrafunctionalized compounds can be successively manipulated to 1,2,3,4,9,10-functionalized [60]fullerene derivatives with an intriguing “ S ”-shaped configuration via a novel electrochemical protonation. Importantly, the stability of tetrafunctionalized [60]fullerene products allows them to be applied in planar perovskite solar cells as efficient electron transport layers.

Neural architecture search (NAS) is a promising method for automatically design neural architectures. NAS adopts a search strategy to explore the predefined search space to find outstanding performance architecture with the minimum searching costs. Bayesian optimization and evolutionary algorithms are two commonly used search strategies, but they suffer from computationally expensive, challenge to implement or inefficient exploration ability. In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is defined from Bayesian optimization and we propose a graph-based uncertainty estimation network as a surrogate model that is easy to implement and computationally efficient. The second predictor is a graph-based neural network that directly outputs the performance prediction of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Extensive experiments demonstrate the superiority of NPENAS. Quantitative results on three NAS search spaces indicate that both NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-BO achieving state-of-the-art performance on NASBench-201 and NPENAS-NP on NASBench-101 and DARTS, respectively.

We propose a self-supervised Gaussian ATtention network for image Clustering (GATCluster). Rather than extracting intermediate features first and then performing the traditional clustering algorithm, GATCluster directly outputs semantic cluster labels without further post-processing. Theoretically, we give a Label Feature Theorem to guarantee the learned features are one-hot encoded vectors, and the trivial solutions are avoided. To train the GATCluster in a completely unsupervised manner, we design four self-learning tasks with the constraints of transformation invariance, separability maximization, entropy analysis, and attention mapping. Specifically, the transformation invariance and separability maximization tasks learn the relationships between sample pairs. The entropy analysis task aims to avoid trivial solutions. To capture the object-oriented semantics, we design a self-supervised attention mechanism that includes a parameterized attention module and a soft-attention loss. All the guiding signals for clustering are self-generated during the training process. Moreover, we develop a two-step learning algorithm that is memory-efficient for clustering large-size images. Extensive experiments demonstrate the superiority of our proposed method in comparison with the state-of-the-art image clustering benchmarks. Our code has been made publicly available at this https URL.

Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for supervised learning. As synthesized metal artifacts in CT images may not accurately reflect the clinical counterparts, an artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. However, as the discriminator can only judge if large regions semantically look artifact-free or artifact-affected, it is difficult for ADN to recover small structural details of artifact-affected CT images based on adversarial losses only without sufficient constraints. To overcome the illposedness of this problem, here we propose a low-dimensional manifold (LDM) constrained disentanglement network (DN), leveraging the image characteristics that the patch manifold of CT images is generally low-dimensional. Specifically, we design an LDM-DN learning algorithm to empower the disentanglement network through optimizing the synergistic loss functions used in ADN while constraining the recovered images to be on a low-dimensional patch manifold. Moreover, learning from both paired and unpaired data, an efficient hybrid optimization scheme is proposed to further improve the MAR performance on clinical datasets. Extensive experiments demonstrate that the proposed LDM-DN approach can consistently improve the MAR performance in paired and/or unpaired learning settings, outperforming competing methods on synthesized and clinical datasets.

Bulk heterojunction (BHJ) perovskite solar cells (PVKSCs) are advantageous in passivating defects and facilitating electron extraction/transport. Most of the reported fullerene derivatives involved in BHJ-PVKSCs render single-site interactions, limiting their passivation effect. Herein, we developed a perfluoroalkyl and pyridine-cofunctionalized fullerene derivative (C60-PyF15), and applied it as an additive of CH3NH3PbI3 layer to construct inverted (p-i-n) BHJ-PVKSCs, affording double-site defect passivation of perovskite film and a champion power conversion efficiency of 20.10%, which is among the highest values for fullerene-incorporated inverted BHJ-PVKSCs. To elucidate the crucial role of the perfluoroalkyl group in efficiency enhancement, another analogous novel non-fluoro-substituted pyridine-functionalized fullerene derivative with the same chain length of alkyl group (C60-PyH15) was also synthesized, which afforded a champion PCE of 19.22% and is lower than that of C60-PyF15. The double-site defect passivation ability of C60-PyF15 is resulted from the coordination interaction of the pyridine moiety with Pb2+ ions and hydrogen bonding between the fluorine atom within the perfluoroalkyl group and the CH3NH3+ cations. These enable ordered orientation of CH3NH3+ and suppressed ion migration, leading to efficiency enhancement and improved ambient and thermal stabilities.

The electrochemical cyclopropanation of [60]fullerobenzofurans with diethyl dibromomalonate has been investigated. Controlled by the steric effect, the sterically favored e bisadducts are obtained as the major products along with two trans-3 bisadducts as minor products. The addition sites and patterns of this reaction are very different from those of our previously reported reaction with benzyl bromide, providing insights into the controlling factors for the electrophilic reactions of dianionic fullerene derivatives.

The regioselective electrosynthesis of tetra- and hexa-functionalized [60]fullerene derivatives with unprecedented 1,2,4,17-(cis-3′) and 1,2,3,4,9,10-(“S”-shaped) addition patterns is achieved., The efficient and regioselective electrosynthesis of tetra- and hexa-functionalized [60]fullerene derivatives with unprecedented addition patterns has been achieved. The tetra-functionalized [60]fullerene derivative with an intriguing 1,2,4,17-addition pattern is regioselectively obtained by cyclization reaction of the dianionic species generated electrochemically from a [60]fulleroindoline with 1,2-bis(bromomethyl)benzene at 0 °C, and can be converted to the more stable 1,2,3,4-adduct at 25 °C. Furthermore, the hexa-functionalized [60]fullerene derivative with the 1,2,3,4,9,10-addition pattern displaying a unique “S”-shaped configuration can be synthesized by protonation of the electrochemically generated dianion of the obtained tetra-functionalized 1,2,4,17-adduct. The structures of the tetra- and hexa-functionalized products have been determined by spectroscopic data and single-crystal X-ray analysis.

Fullerene is the first molecular form of the elemental carbon, and its discovery more than three decades ago substantially revolutionalized nanoscience. One of the most intriguing structural feature of fullerene is its hollow interior which forms a closed cage; such a sub-nanomater inner space offers unique opportunities to entrap atoms, ions, or clusters, affording endohedral fullerenes. Meanwhile, the spherical skeleton of fullerene consists of a conjugated π system, enabling the feasibility of exohedral functionalizations of fullerene to utilize its outer space. Herein, we present a brief overview on recent advance in both endohedral and exohedral functionalizations of fullerenes as well as their potential applications in versatile fields.

Deep learning has become the most powerful tool for action recognition in recent years. The aim of this paper is to investigate the proper convolutional network architecture to classify poleward moving auroras (PMAs). The first challenge is that the auroral images have complex morphological and motion characteristics, so it is difficult to get the discriminative motion information to recognize the PMA events. Second, the imbalanced dataset will cause the serious problems, such as the network tends to identify a given sequence as the majority of events (non-PMAs). To address these issues, we use 3D ResNet-18 to get the motion information of auroral image sequence based on optical flow and also use the spatial attention mechanism to make the network pay more attention to PMA events. The empirical results demonstrate that our method achieves significant performance improvement compared with the previous method.

[60]Fullerene-fused lactones can be electrochemically reduced under controlled potential electrolysis (PCE) to generate the corresponding dianionic species. The generated dianionic [60]fullerene-fused lactones are then treated with acids and different electrophiles to afford the [60]fullerene-fused ketones, alkylated 1,2- and 1,4-adducts, and cyclic adducts with a medium or large ring.

Non-isothermal oxidation kinetics of the high-speed steel (HSS) were studied by thermal gravimetric analysis. The surface and cross-sectional morphology of the HSS oxide film formed at different temperatures and durations were observed by scanning electron microscopy, and the corresponding chemical composition was analyzed by using energy dispersive spectrometer. The composition and structure of the oxide film were also investigated by X-ray diffraction. The results showed that the oxide scale of the HSS is mainly composed of Fe2O3, Fe3O4 and FeCr2O4. Temperature is the main factor on the quality of the oxide film. Below 600 °C, the oxidation rate of the steel is slow and the thickness of the oxide film is below 5 µm. However, the oxidation rate sharply increased as the temperature reaches 600 °C. Cr, Mo and V tend to concentrate at the scale/steel interface and form FeCr2O4 and other oxides.

The cupping tests under different rate demonstrated that there was a correlation between the plastic deformation and shaping time of transformation induced plasticity (TRIP) steel, illustrating that there was also the rheology in the process of plastic forming for solid metal materials. The creep experiments were carried out by Gleeble 3500 thermal simulated test machine, and Mises yield rule was used to verify the creep experiments satisfying the visco-plastic conditions when the load was greater than yield strength. The visco-plastic deformation rate of creep experiments was obtained based on Bingham model. The viscous correlation coefficient (γ) was deduced, reaching that the viscosity of TRIP steel shows deformation resistance in the process of plastic shaping. These results provide the theoretical basis for increasing the plate yield and controlling the forming rate.