Unsupervised Domain Adaptation with Similarity Learning

The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution. Most deep learning approaches to domain adaptation consist of two steps: (i) learn features that preserve a low risk on labeled samples (source domain) and (ii) make the features from both domains to be as indistinguishable as possible, so that a classifier trained on the source can also be applied on the target domain. In general, the classifiers in step (i) consist of fully-connected layers applied directly on the indistinguishable features learned in (ii). In this paper, we propose a different way to do the classification, using similarity learning. The proposed method learns a pairwise similarity function in which classification can be performed by computing similarity between prototype representations of each category. The domain-invariant features and the categorical prototype representations are learned jointly and in an end-to-end fashion. At inference time, images from the target domain are compared to the prototypes and the label associated with the one that best matches the image is outputed. The approach is simple, scalable and effective. We show that our model achieves state-of-the-art performance in different unsupervised domain adaptation scenarios

Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts

Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary substantially due to environment changes and sensor noises, but deep models only know the training domain style. Such domain style gap impedes model generalization on diverse real-world domains. Our proposed Normalization Perturbation (NP) can effectively overcome this domain style overfitting problem. We observe that this problem is mainly caused by the biased distribution of low-level features learned in shallow CNN layers. Thus, we propose to perturb the channel statistics of source domain features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training. We further explore the style-sensitive channels for effective style synthesis. Normalization Perturbation only relies on a single source domain and is surprisingly effective and extremely easy to implement. Extensive experiments verify the effectiveness of our method for generalizing models under real-world domain shifts

Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts

Improving model's generalizability against domain shifts is crucial,especially for safety-critical applications such as autonomous driving.Real-world domain styles can vary substantially due to environment changes andsensor noises, but deep models only know the training domain style. Such domainstyle gap impedes model generalization on diverse real-world domains. Ourproposed Normalization Perturbation (NP) can effectively overcome this domainstyle overfitting problem. We observe that this problem is mainly caused by thebiased distribution of low-level features learned in shallow CNN layers. Thus,we propose to perturb the channel statistics of source domain features tosynthesize various latent styles, so that the trained deep model can perceivediverse potential domains and generalizes well even without observations oftarget domain data in training. We further explore the style-sensitive channelsfor effective style synthesis. Normalization Perturbation only relies on asingle source domain and is surprisingly effective and extremely easy toimplement. Extensive experiments verify the effectiveness of our method forgeneralizing models under real-world domain shifts.<br

Improving Cancer Classification With Domain Adaptation Techniques

Background: As the quantity and complexity of oncological data continue to increase, machine learning (ML) has become an important tool in helping clinicians better understand malignancies and provide personalized care. Diagnostic image analysis, in particular, has benefited from the advent of ML methods to improve image classification and generate prognostic information from imaging collected in routine clinical practice [1-3]. Deep learning, a subset of ML, has especially achieved remarkable performance in medical imaging, including segmentation [4, 5], object detection, classification [6], and diagnosis [7]. Despite the notable success of deep learning computer vision models on oncologic imaging data, recent studies have identified notable weaknesses in deep learning models used on diagnostic images. Specifically, deep learning models have difficulty generalizing to data that was not well represented during training. One potential solution is the use of domain adaptation (DA) techniques, which improve the generalizability of a deep learning model trained on one domain to better generalize to data of a target domain. Techniques: In this study, we explain the efficacy of four common DA techniques – MMD, CORAL, iDANN, and AdaBN - used on deep learning models trained on common diagnostic imaging modalities in oncology. We used two datasets of mammographic imaging and CT scans to test the prediction accuracy of models using each of these DA techniques and compared them to the performance of transfer learning. Results: In the mammographic imaging data, MMD, CORAL, and iDANN increased the target test accuracy for all four domains. MMD increased target accuracies by 3.6 - 45%, CORAL by 4- 48%, and iDANN by 1.5-49.4%. For the CT scan dataset, MMD, CORAL, and iDANN increased the target test accuracy for all domains. MMD increased the target accuracy by 2.0 – 13.9%, CORAL by 2.4 - 15.8%, and iDANN by 2.0 – 11.1%. in both the mammographic imaging data and the CT scans, AdaBN performed worse or comparably to transfer learning. Conclusion: We found that DA techniques significantly improve model performance and generalizability. These findings suggest that there’s potential to further study how multiple DA techniques can work together and that these can potentially help us create more robust, generalizable models

Object Detection Frameworks for Fully Automated Particle Picking in Cryo-EM

Particle picking in cryo-EM is a form of object detection for noisy, low contrast, and out-of-focus microscopy images, taken of different (unknown) structures. This thesis presents a fully automated approach which, for the first time, explicitly considers training on multiple structures, while simultaneously learning both specialized models for each structure used for training and a generic model that can be applied to unseen structures. The presented architecture is fully convolutional and divided into two parts: (i) a portion which shares its weights across all structures and (ii) N+1 parallel sets of sub-architectures, N of which are specialized to the structures used for training and a generic model whose weights are tied to the layers for the specialized models. Experiments reveal improvements in multiple use cases over the-state-of-art and present additional possibilities to practitioners