Synthetic Augmentation with Large-scale Unconditional Pre-training

Deep learning based medical image recognition systems often require a substantial amount of training data with expert annotations, which can be expensive and time-consuming to obtain. Recently, synthetic augmentation techniques have been proposed to mitigate the issue by generating realistic images conditioned on class labels. However, the effectiveness of these methods heavily depends on the representation capability of the trained generative model, which cannot be guaranteed without sufficient labeled training data. To further reduce the dependency on annotated data, we propose a synthetic augmentation method called HistoDiffusion, which can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training. In particular, we train a latent diffusion model (LDM) on diverse unlabeled datasets to learn common features and generate realistic images without conditional inputs. Then, we fine-tune the model with classifier guidance in latent space on an unseen labeled dataset so that the model can synthesize images of specific categories. Additionally, we adopt a selective mechanism to only add synthetic samples with high confidence of matching to target labels. We evaluate our proposed method by pre-training on three histopathology datasets and testing on a histopathology dataset of colorectal cancer (CRC) excluded from the pre-training datasets. With HistoDiffusion augmentation, the classification accuracy of a backbone classifier is remarkably improved by 6.4% using a small set of the original labels. Our code is available at https://github.com/karenyyy/HistoDiffAug.Comment: MICCAI 202

A Highly Controllable Electrochemical Anodization Process to Fabricate Porous Anodic Aluminum Oxide Membranes

Due to the broad applications of porous alumina nanostructures, research on fabrication of anodized aluminum oxide (AAO) with nanoporous structure has triggered enormous attention. While fabrication of highly ordered nanoporous AAO with tunable geometric features has been widely reported, it is known that its growth rate can be easily affected by the fluctuation of process conditions such as acid concentration and temperature during electrochemical anodization process. To fabricate AAO with various geometric parameters, particularly, to realize precise control over pore depth for scientific research and commercial applications, a controllable fabrication process is essential. In this work, we revealed a linear correlation between the integrated electric charge flow throughout the circuit in the stable anodization process and the growth thickness of AAO membranes. With this understanding, we developed a facile approach to precisely control the growth process of the membranes. It was found that this approach is applicable in a large voltage range, and it may be extended to anodization of other metal materials such as Ti as well.Hong Kong Research Grant Council [612113]; Hong Kong Innovation Technology Commission [ITS/362/14FP]; Fundamental Research Project of Shenzhen Science & Technology Foundation [JCYJ20130402164725025]; National Natural Science Foundation of China [61574005]; Priority Academic Program Development of Jiangsu Higher Education Institutions [PAPD]SCI(E)[email protected]; [email protected]

Stochastic multiple attribute decision making with Pythagorean hesitant fuzzy set based on regret theory

The objective of this paper is to present an extended approach to address the stochastic multi-attribute decision-making problem. The novelty of this study is to consider the regret behavior of decision makers under a Pythagorean hesitant fuzzy environment. First, the group satisfaction degree of decision-making matrices is used to consider the different preferences of decision-makers. Second, the nonlinear programming model under different statues is provided to compute the weights of attributes. Then, based on the regret theory, a regret value matrix and a rejoice value matrix are constructed. Furthermore, the feasibility and superiority of the developed approach is proven by an illustrative example of selecting an air fighter. Eventually, a comparative analysis with other methods shows the advantages of the proposed methods