Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost of establishing high-quality medical datasets. Many FSL approaches have been proposed in natural image scenes. However, present FSL methods are rarely evaluated on medical images and the FSL technology applicable to medical scenarios need to be further developed. Meta-learning has supplied an optional framework to address the challenging FSL setting. In this paper, we propose a novel multi-learner based FSL method for multiple medical image classification tasks, combining meta-learning with transfer-learning and metric-learning. Our designed model is composed of three learners, including auto-encoder, metric-learner and task-learner. In transfer-learning, all the learners are trained on the base classes. In the ensuing meta-learning, we leverage multiple novel tasks to fine-tune the metric-learner and task-learner in order to fast adapt to unseen tasks. Moreover, to further boost the learning efficiency of our model, we devised real-time data augmentation and dynamic Gaussian disturbance soft label (GDSL) scheme as effective generalization strategies of few-shot classification tasks. We have conducted experiments for three-class few-shot classification tasks on three newly-built challenging medical benchmarks, BLOOD, PATH and CHEST. Extensive comparisons to related works validated that our method achieved top performance both on homogeneous medical datasets and cross-domain datasets.

Existing multi-view learning methods based on the information bottleneck principle exhibit impressing generalization by capturing inter-view consistency and complementarity. They leverage cross-view joint information (consistency) and view-specific information (complementarity) while discarding redundant information. By fusing visual features, multi-view learning methods help medical image processing to produce more reliable predictions. However, multi-views of medical images often have low consistency and high complementarity due to modal differences in imaging or different projection depths, thus challenging existing methods to balance them to the maximal extent. To mitigate such an issue, we improve the information bottleneck (IB) loss function with a balanced regularization term, termed IBB loss, reassembling the constraints of multi-view consistency and complementarity. In particular, the balanced regularization term with a unique trade-off factor in IBB loss helps minimize the mutual information on consistency and complementarity to strike a balance. In addition, we devise a triplet multi-view network named TM net to learn the consistent and complementary features from multi-view medical images. By evaluating two datasets, we demonstrate the superiority of our method against several counterparts. The extensive experiments also confirm that our IBB loss significantly improves multi-view learning in medical images.

It turns out that convolutional neural networks (CNNs) have excellent medical image processing capabilities. Hence, effectively and efficiently deploying CNNs on devices with varying computing power to make computer-aided diagnosis puts on the agenda. However, it is a dilemma to balance the limited computing resources and model complexity. Previously, we proposed factorized convolution with spectral normalization (FConvSN) to mitigate the bottleneck of deploying CNNs for 2D medical images. But due to the cube structure of 3D convolutional kernels, it does not work well for 3D medical images. Directly flattening 3D kernels to 2D weights for matrix factorization may undermine the learning ability along depth-wise, resulting in the loss of depth information and the decline of model performance. To this end, we factorize a 3D convolutional kernel to 2D weight matrices with depth-wise dimensions, then assign an attentive score for each 2D weight matrix by a depth-wise dependencies-induced adaptive attention block (AA). AA with a temperature hyper-parameter helps convolution kernel to better capture depth-wise dependencies in 3D medical images, improving its learning ability along the depth direction. We term this novel factorized convolution as FConvAA used for compressing model complexity without impairing the depth-wise expressivity. We also impose spectral normalization (SN) for FConvAA to constrain spectral norm-wise weights. We conduct extensive experiments on the public lung CT dataset LUNA16 and the private retina OCT dataset to demonstrate the effectiveness and feasibility of our FConvAA.

Cataracts are the leading cause of vision loss worldwide. Restoration algorithms are developed to improve the readability of cataract fundus images in order to increase the certainty in diagnosis and treatment for cataract patients. Unfortunately, the requirement of annotation limits the application of these algorithms in clinics. This paper proposes a network to annotation-freely restore cataractous fundus images (ArcNet) so as to boost the clinical practicability of restoration. Annotations are unnecessary in ArcNet, where the high-frequency component is extracted from fundus images to replace segmentation in the preservation of retinal structures. The restoration model is learned from the synthesized images and adapted to real cataract images. Extensive experiments are implemented to verify the performance and effectiveness of ArcNet. Favorable performance is achieved using ArcNet against state-of-the-art algorithms, and the diagnosis of ocular fundus diseases in cataract patients is promoted by ArcNet. The capability of properly restoring cataractous images in the absence of annotated data promises the proposed algorithm outstanding clinical practicability.