Recently, contrastive learning has received significant attention in various classification tasks of natural images. However, current contrastive learning frameworks display unsatisfactory performance on medical images due to the inability of obtaining fine-grained visual features. In this paper, we propose a Lesion-Aware Contrastive Learning (LACL) framework to learn more discriminative and comprehensive representations and reinforce the attention of diagnosis regions on medical images. LACL framework includes two phases of training procedures: the comprehensive feature-extracting phase and the contrastive learning enhancement phase. In the first phase, LACL fully captures meaningful deep features related to the training targets to form comprehensive visual representations, by training a novel lesion-aware module we proposed. In the second phase, we introduce the previous representation information into contrastive learning to guide the LACL framework in learning disease- related features. This approach provides more effective guidance than the traditional contrastive learning method of directly comparing features. Extensive experiments on several benchmark datasets demonstrate that our LACL framework significantly improves the performance of medical image classification and highlights the lesion areas for disease diagnosis.

A nuclear cataract is an age-related chronic and priority ophthalmic disease in which a clouding of the lens in the human eye affects vision. Automatic segmentation of nuclear region based on slit-lamp photographs is a basic step for computer-aided diagnosis such as nuclear cataract grading. However, slit-lamp photographs collected from a clinic scenario often have complex background containing the eyelids, sclera and cornea with spectral highlights. The existing efforts using traditional image processing that have unsatisfactory results, and the deep learning method using standard Faster R-CNN tends to obtain a bigger nuclear contour. In this paper, we propose a coarse-to-fine deep learning solution to localize nuclear regions by cascading the Faster R-CNN in a two-stage framework. First, a nuclear ROI (region of interest) predictor is pre-trained to localize a rough position and remove complex backgrounds. Then, a fine nuclear locator is applied to predict a more compact nuclear bounding box. Finally, an ellipse-like nuclear contour is fitted based on its bounding box. Evaluated on a clinical dataset of 884 slit-lamp photographs, the proposed method outperforms the state-of-the-art, improving the overlapping rate (IoU) by 0.33% from 67.98% to 68.31%, and increasing the success rate by 2.55% from 85.71% to 88.26%.

Sports videos have special characteristics such as well-defined video structure, specialized sports syntax, and typically having some canonical view types. In this paper, we propose a semi-supervised incremental learning framework for sports video view classification. Baseball is selected as an example to explain the main ideas. In order to obtain an optimal model based on a small number of pre-labeled training samples, the semi-supervised incremental learning framework explores the local distributed properties of the video sequences and sufficiently utilizes the information of a positive model pool and a negative model pool. After each round of online optimization process for the under-investigating video, a locally-optimized positive model and a set of negative models are added into the positive model pool and the negative model pool according to some heuristic criteria, respectively. Experiments results on real sports video data show that the proposed system is effective and promising

This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic classification framework, termed OOIL (for Online-Optimized Incremental Learning), in which two sets of optimized classification models, local and global, are online trained by sufficiently exploiting both local and global statistic characteristics of videos. The global models are pre-trained on a relatively small set of pre-labeled samples. And the local models are optimized for the under-test video or video segment by checking a small portion of unlabeled samples in this video, while they are also applied to incrementally update the global models. Experiments have illustrated promising results on simulated data as well as real sports videos.