Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have instance-dependent noise (IDN) and suffer from high observer variability. In this paper, we systematically discuss the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from biased aggregation of individual annotations. We then propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task. We design a dual-uncertainty estimation approach to measure the disagreement label noise and single-target label noise via improved Direct Uncertainty Prediction and Monte- Carlo-Dropout. A boosting-based curriculum training procedure is later introduced for robust learning. We demonstrate the effectiveness of our method by conducting extensive experiments on three different diseases with synthesized and real-world label noise: skin lesions, prostate cancer, and retinal diseases. We also release a large re-engineered database that consists of annotations from more than ten ophthalmologists with an unbiased golden standard dataset for evaluation and benchmarking. The dataset is available at https://mmai.group/peoples/julie/.

Entropy minimization has been widely used in unsupervised domain adaptation (UDA). However, existing works reveal that the use of entropy-minimization-only may lead to collapsed trivial solutions for UDA. In this article, we try to seek possible close-to-ideal UDA solutions by focusing on some intuitive properties of the ideal domain adaptation solution. In particular, we propose to introduce diversity maximization for further regulating entropy minimization. In order to achieve the possible minimum target risk for UDA, we show that diversity maximization should be elaborately balanced with entropy minimization, the degree of which can be finely controlled with the use of deep embedded validation in an unsupervised manner. The proposed minimal-entropy diversity maximization (MEDM) can be directly implemented by stochastic gradient descent without the use of adversarial learning. Empirical evidence demonstrates that MEDM outperforms the state-of-the-art methods on four popular domain adaptation datasets.

Lightweight semantic segmentation plays an essential role in image signal processing that is beneficial to many multimedia applications, such as self-driving, robotic vision, and virtual reality. Due to the powerful capability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years for semantic segmentation. In spite of achieving remarkable progresses, they often ignore semantic context ranged from different scales. Furthermore, most of them always neglect the object boundaries, serving as a significant assistance for lightweight semantic segmentation. To alleviate these problems, this paper develops a Boundary-guide dual-resolution lightweight network with multi-scale Semantic Context, called BSCNet, for semantic segmentation. Specifically, to enhance the capability of feature representation, an Extremely Lightweight Pyramid Pooling Module (ELPPM) is designed to capture multi-scale semantic context at the top of low-resolution branch of BSCNet. In addition, to increase feature similarity of the same object while keeping feature discrimination of different objects, pixel information is propagated throughout the entire object area using a simple Boundary Auxiliary Fusion Module (BAFM), where the predicted object boundaries are served as high-level guidance to refine low-level convolutional features. The comprehensive experimental results have demonstrated that our BSCNet is simple and effective, achieving state-of-the-art trade-off in terms of segmentation accuracy and running efficiency on CityScapes, CamVid, and KITTI datasets.