Binary Representation via Jointly Personalized Sparse Hashing

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g. color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, firstly, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our JPSH, we incorporate the PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters, but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. Extensive experiments on four benchmark datasets verify that the JPSH outperforms several hashing algorithms on the similarity search task.

CE-based white-box adversarial attacks will not work using super-fitting

Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation to the input will make the model obtain wrong results. This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i.e., the accuracy under adversarial attacks). This paper mathematically proves the effectiveness of super-fitting and enables the model to reach this state quickly by minimizing unrelated category scores (MUCS). Theoretically, super-fitting can resist any existing (even future) CE-based white-box adversarial attacks. In addition, this paper uses a variety of powerful attack algorithms to evaluate the adversarial robustness of super-fitting, and the proposed method is compared with nearly 50 defense models from recent conferences. The experimental results show that the super-fitting method in this paper can make the trained model obtain the highest adversarial robustness.

Rethinking Classifier and Adversarial Attack

Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To solve this problem, this paper uses the proposed decouple space method to divide the classifier into two parts: non-linear and linear. Then, this paper defines the representation vector of the original example (and its space, i.e., the representation space) and uses the iterative optimization of Absolute Classification Boundaries Initialization (ACBI) to obtain a better attack starting point. Particularly, this paper applies ACBI to nearly 50 widely-used defense models (including 8 architectures). Experimental results show that ACBI achieves lower robust accuracy in all cases.

Rethinking Classifier And Adversarial Attack

Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e. not approaching the lower bound of robustness). To solve this problem, this paper first uses the Decouple Space method to divide the classifier into two parts: non-linear and linear. On this basis, this paper defines the representation vector of original example (and its space, i.e., the representation space) and uses Absolute Classification Boundaries Initialization (ACBI) iterative optimization to obtain a better attack starting point (i.e. attacking from this point can approach the lower bound of robustness faster). Particularly, this paper apply ACBI to nearly 50 widely-used defense models (including 8 architectures). Experimental results show that ACBI achieves lower robust accuracy in all cases.

Based-CE white-box adversarial attack will not work using super-fitting

Deep Neural Networks (DNN) are widely used in various fields due to their powerful performance, but recent studies have shown that deep learning models are vulnerable to adversarial attacks-by adding a slight perturbation to the input, the model will get wrong results. It is especially dangerous for some systems with high security requirements, so this paper proposes a new defense method by using the model super-fitting status. Model's adversarial robustness (i.e., the accuracry under adversarial attack) has been greatly improved in this status. This paper mathematically proves the effectiveness of super-fitting, and proposes a method to make the model reach this status quickly-minimaze unrelated categories scores (MUCS). Theoretically, super-fitting can resist any existing (even future) Based on CE white-box adversarial attack. In addition, this paper uses a variety of powerful attack algorithms to evaluate the adversarial robustness of super-fitting and other nearly 50 defense models from recent conferences. The experimental results show that super-fitting method in this paper can make the trained model obtain the highest adversarial performance robustness.

Weakly-Supervised Feature Learning via Text and Image Matching

When training deep neural networks for medical image classification, obtaining a sufficient number of manually annotated images is often a significant challenge. We propose to use textual findings, which are routinely written by clinicians during manual image analysis, to help overcome this problem. The key idea is to use a contrastive loss to train image and text feature extractors to recognize if a given image-finding pair is a true match. The learned image feature extractor is then fine-tuned, in a transfer learning setting, for a supervised classification task. This approach makes it possible to train using large datasets because pairs of images and textual findings are widely available in medical records. We evaluate our method on three datasets and find consistent performance improvements. The biggest gains are realized when fewer manually labeled examples are available. In some cases, our method achieves the same performance as the baseline even when using 70\%--98\% fewer labeled examples.

Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification

Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy, ignoring the important role of uncertainty quantification. Empirically, neural networks are often miscalibrated and overconfident in their predictions. This miscalibration could be problematic in any automatic decision-making system, but we focus on the medical field in which neural network miscalibration has the potential to lead to significant treatment errors. We propose a novel calibration approach that maintains the overall classification accuracy while significantly improving model calibration. The proposed approach is based on expected calibration error, which is a common metric for quantifying miscalibration. Our approach can be easily integrated into any classification task as an auxiliary loss term, thus not requiring an explicit training round for calibration. We show that our approach reduces calibration error significantly across various architectures and datasets.

Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization

Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on publicly available datasets often do not work well on real-world clinical data due to the differences in patient population and image device configurations. Also, manually annotating clinical images is expensive. In this work, we propose an unsupervised domain adaptation (UDA) method using Cycle-GAN to improve the generalization ability of the model without using any additional manual annotations.

Joint 2D-3D Breast Cancer Classification

Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification

Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.