With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.

Multi-modal imaging data fusion has attracted much attention in medical data analysis because it can provide complementary information for more accurate analysis. Integrating functional and structural multi-modal imaging data has been increasingly used in the diagnosis of brain diseases, such as epilepsy. Most of the existing methods focus on the feature space fusion of different modalities but ignore the valuable high-order relationships among samples and the discriminative fused features for classification. In this paper, we propose a novel framework by fusing data from two modalities of functional MRI (fMRI) and diffusion tensor imaging (DTI) for epilepsy diagnosis, which effectively captures the complementary information and discriminative features from different modalities by high-order feature extraction with the attention mechanism. Specifically, we propose a triple network to explore the discriminative information from the high-order representation feature space learned from multi-modal data. Meanwhile, self-attention is introduced to adaptively estimate the degree of importance between brain regions, and the cross-attention mechanism is utilized to extract complementary information from fMRI and DTI. Finally, we use the triple loss function to adjust the distance between samples in the common representation space. We evaluate the proposed method on the epilepsy dataset collected from Jinling Hospital, and the experiment results demonstrate that our method is significantly superior to several state-of-the-art diagnosis approaches.

Multi-modal fusion has become an important data analysis technology in Alzheimer’s disease (AD) diagnosis, which is committed to effectively extract and utilize complementary information among different modalities. However, most of the existing fusion methods focus on pursuing common feature representation by transformation, and ignore discriminative structural information among samples. In addition, most fusion methods use high-order feature extraction, such as deep neural network, by which it is difficult to identify biomarkers. In this paper, we propose a novel method named deep multi-modal discriminative and interpretability network (DMDIN), which aligns samples in a discriminative common space and provides a new approach to identify significant brain regions (ROIs) in AD diagnosis. Specifically, we reconstruct each modality with a hierarchical representation through multilayer perceptron (MLP), and take advantage of the shared self-expression coefficients constrained by diagonal blocks to embed the structural information of inter-class and the intra-class. Further, the generalized canonical correlation analysis (GCCA) is adopted as a correlation constraint to generate a discriminative common space, in which samples of the same category gather while samples of different categories stay away. Finally, in order to enhance the interpretability of the deep learning model, we utilize knowledge distillation to reproduce coordinated representations and capture influence of brain regions in AD classification. Experiments show that the proposed method performs better than several state-of-the-art methods in AD diagnosis.

Multi-modal emotion recognition has attracted increasing attention in human-computer interaction, as it extracts complementary information from physiological and behavioral features. Compared to single modal approaches, multi-modal fusion methods are more susceptible to uncertainty in emotion recognition, such as heterogeneity and inconsistent predictions across different modalities. Previous multi-modal approaches ignore systematic modeling of uncertainty in fusion and revelation of dynamic variations in emotion process. In this paper, we propose a dynamic confidence-aware fusion network for robust recognition of heterogeneous emotion features, including electroencephalogram (EEG) and facial expression. First, we develop a self-attention based multi-channel LSTM network to preliminarily align the heterogeneous emotion features. Second, we propose a confidence regression network to estimate true class probability (TCP) on each modality, which helps explore the uncertainty at modality level. Then, different modalities are weighted fused according to above two types of uncertainty. Finally, we adopt self-paced learning (SPL) mechanism to further improve the model robustness by alleviating negative effect from the hard learning samples. The experimental results on several multi-modal emotion datasets demonstrate the proposed method outperforms the state-of-the-art methods in emotion recognition performance and explicitly reveals the dynamic variation of emotion with uncertainty estimation. Our code is available at:

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting features from independent brain networks divided by sliding windows, making them hard to reveal the high-order dynamic evolution laws of functional brain networks. Additionally, they cannot effectively extract the spatio-temporal topology features in dynamic brain networks. In this paper, we propose to use optimal transport (OT) theory to capture the topology evolution of the dynamic brain networks, and develop a multi-channel spatio-temporal graph convolutional network that collaboratively extracts the temporal and spatial features from the evolution networks. Specifically, we first adaptively evaluate the graph hubness of brain regions in the brain network of each time window, which comprehensively models information transmission among multiple brain regions. Second, the hubness propagation information across adjacent time windows is captured by optimal transport, describing high-order topology evolution of dynamic brain networks. Moreover, we develop a spatio-temporal graph convolutional network with attention mechanism to collaboratively extract the intrinsic temporal and spatial topology information from the above networks. Finally, the multi-layer perceptron is adopted for classifying the dynamic brain network. The extensive experiment on the collected epilepsy dataset and the public ADNI dataset show that our proposed method not only outperforms several state-of-the-art methods in brain disease diagnosis, but also reveals the key dynamic alterations of brain connectivities between patients and healthy controls.

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional magnetic resonance imaging based schizophrenia diagnosis. However, previous studies usually measure the fALFF with specific bands from 0.01-0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. As we konow, fALFF data is intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multi-frequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multi-frequency bands (i.e., slow-5:0.01-0.027 Hz, slow-4:0.027-0.073 Hz, slow-3:0.073-0.198 Hz and slow-2:0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches, and select those significant patches related to schizophrenia using random forest-based importancescore. Moreover, we use tree-structured sparse learning method for feature selection with above patches spatial constraint. Finally, considering biomarkers from multi-frequency bands can reflect complementary information among multiple frequency bands, we adopt the multi-kernel learning (MKL) method to combine features of multi-frequency bands for classification. Our experimental results show that these biomarkers from multi-frequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating the multi-frequency bands analysis can better account for classification of schizophrenia.