Schizophrenia is a severe mental disease that affects patients' thoughts, feelings, and behaviors. Speech signal has proven to be a biomarker in the early diagnosis of schizophrenia. Previous studies on schizophrenic speech detection are mainly based on manual feature extraction engineering, which requires domain knowledge for researchers and has difficulties extracting effective features. This work proposes an end-to-end architecture, called Axial-attention-based Network using Wideband and Narrowband Spectrograms (WNSA-Net), to detect schizophrenia. Specifically, we adopt both wideband and narrowband spectrograms as inputs to represent speech signals using fine time and frequency structures. Then dilated convolution blocks are employed to capture detailed and long-range information in spectrograms. Axial-attention blocks are introduced to augment the information in feature maps along the time and frequency axes. In addition, we employ a gate mechanism to fuse the output feature maps from all channels. Experimental results on the Schizophrenia dataset and its subdatasets show that schizophrenic patients have difficulties in expressing emotions. To validate the performance of our WNSA-Net, experiments are conducted on Schizophrenia dataset and open-access TORGO database, achieving 97.37% and 98.16% accuracy in detecting schizophrenia and dysarthria, respectively. The results show promise for the proposed method in the diagnosis of disordered speech.

Weakly-supervised whole-slide image (WSI) classification (WSWC) is a challenging task where a large number of unlabeled patches (instances) exist within each WSI (bag) while only a slide label is given. Despite recent progress for the multiple instance learning (MIL)-based WSI analysis, the major limitation is that it usually focuses on the easy-to-distinguish diagnosis-positive regions while ignoring positives that occupy a small ratio in the entire WSI. To obtain more discriminative features, we propose a novel weakly-supervised classification method based on cross-slide contrastive learning (called SCL-WC), which depends on task-agnostic self-supervised feature pre-extraction and task-specific weakly-supervised feature refinement and aggregation for WSI-level prediction. To enable both intra-WSI and inter-WSI information interaction, we propose a positive-negative-aware module (PNM) and a weakly-supervised cross-slide contrastive learning (WSCL) module, respectively. The WSCL aims to pull WSIs with the same disease types closer and push different WSIs away. The PNM aims to facilitate the separation of tumor-like patches and normal ones within each WSI. Extensive experiments demonstrate state-of-the-art performance of our method in three different classification tasks (e.g., over 2% of AUC in Camelyon16, 5% of F1 score in BRACS, and 3% of AUC in DiagSet). Our method also shows superior flexibility and scalability in weakly-supervised localization and semi-supervised classification experiments (e.g., first place in the BRIGHT challenge). Our code will be available at https://github.com/Xiyue-Wang/SCL-WC.

Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental disorders. Hyperactivity is a typical symptom of ADHD in children. Clinicians diagnose this symptom by evaluating the children's activities based on subjective rating scales and clinical experience. In this work, an objective system is proposed to quantify the movements of children with ADHD automatically. This system presents a new movement detection and quantification method based on depth images. A novel salient object extraction method is proposed to segment body regions. In movement detection, we explore a new local search algorithm to detect any potential motions of children based on three newly designed evaluation metrics. In the movement quantification, two parameters are investigated to quantify the participation degree and the displacements of each body part in the movements. This system is tested by a depth dataset of children with ADHD. The movement detection results of this dataset mainly range from 91.0% to 95.0%. The movement quantification results of children are consistent with the clinical observations. The public MSR Action 3D dataset is tested to validate the performance of this system.

Affective flattening is a typical negative symptom in schizophrenia that causes a diminution of normal behaviors and functions in schizophrenic patients. In this work, an automatic algorithm of schizophrenia detection is proposed. This algorithm includes three newly proposed features. These features establish a representation for the production and perception of schizophrenic speech, called the K-Sf (kurtosis skewness fraction), SPI (sound perception indicator), and EBMC (enhanced bilateral matching coefficient). The K-Sf is proposed to reflect the overall distribution of speech segments. The SPI evaluates the relation between the speech composition and the sound perception. The EBMC feature is proposed based on the theory of air pressure oscillations which could reflect the details of air modulation in the vocal tract. Experiments evaluating the discriminative capabilities of the three features are conducted using a speech dataset that is collected from 56 participants (28 schizophrenic patients and 28 healthy controls) and an ensemble classifier. Comparative experiments with SVM classifiers are also conducted. The discrimination accuracies of patients and control subjects using the K-Sf, SPI, EBMC, and the ensemble classifier are in the range of 76.8-92.9%, 82.1-92.8%, and 80.4-91.1%, respectively. When the three features are combined, the discrimination results range from 89.3% to 94.6%. The experimental results indicate that the three features have stronger robustness and better discrimination capability than those previous features relating to the detection of flat affect.

The automatic evaluation of CP (Cleft Plate) speech has various clinical applications. In this work, automatic classification of hypernasality levels and detection of consonant omission methods are proposed. Considering that the data collection is a major bottleneck in the field of CP speech signal processing, an extensive CP speech database is used. This database is collected over 10 years by the Hospital of Stomatology, Sichuan University, which has the largest number of CLP (Cleft Lip and Palate) patients in China. The vocabulary used for this database covers all the initial consonants and the most widely used vowels in Mandarin. Based on the production process of hypernasality, a two-step classification algorithm is proposed to identify four levels of hypernasality: normal, mild, moderate and severe. In order to locate consonant omission, an effective classification algorithm is proposed through analyzing the acoustic characteristics of initial consonants and finals. The classification accuracy for four hypernasality levels reaches up to 83% and the correct identification of consonant omission is over 94%.