Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dysarthric speech. The experiments were carried out with the popularly used UA-speech database. In the detection experiments, the results revealed that the best performance was obtained using the embeddings from the first layer of the wav2vec model that yielded an absolute improvement of 1.23% in accuracy compared to the best performing baseline feature (spectrogram). In the studied severity level classification task, the results revealed that the embeddings from the final layer gave an absolute improvement of 10.62% in accuracy compared to the best baseline features (mel-frequency cepstral coefficients).

Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel features derived from the single frequency filtering (SFF) method: (1) SFF cepstral coefficients (SFFCC) and (2) MFCCs from the SFF (MFCC-SFF) for the severity classification of PD. Prior studies have demonstrated that SFF offers greater spectro-temporal resolution compared to the short-time Fourier transform. The study uses the PC-GITA database, which includes speech of PD patients and healthy controls produced in three speaking tasks (vowels, sentences, text reading). Experiments using the SVM classifier revealed that the proposed features outperformed the conventional MFCCs in all three speaking tasks. The proposed SFFCC and MFCC-SFF features gave a relative improvement of 5.8% and 2.3% for the vowel task, 7.0% & 1.8% for the sentence task, and 2.4% and 1.1% for the read text task, in comparison to MFCC features.

This upload contains the neural networks used in the paper "Improving filling level classification with adversarial training". The networks are already pre-trained on the 3 splits (S1, S2, S3) of the C-CCM dataset, using six different training strategies. The networks are implemented in PyTorch. More information regarding the C-CCM dataset can be found here: https://corsmal.eecs.qmul.ac.uk/filling.html The CCM_Filling_Level_Pretrained_Models.zip file contains: 3 folders (S1, S2, S3) that correspond to the different dataset splits Each of S1, S2, S3 folders contains 6 subfolders (ST, AT, ST-FT, ST-AFT, AT-FT, AT-AFT) which correspond to the different training strategies used in the paper. Each of the ST, AT, ..., AT-AFT subfolders contains a PyTorch file named last.t7. This is the PyTorch ResNet-18 model that is trained on the corresponding split (S1/S2/S3) using the corresponding training strategy (ST, AT, ..., AT-AFT). A Python example script for loading the models is also provided (load_model.py).

In the last part, we started an introductory discussion on the present state of Deep Learning in histopathology. In the last part, we started an introductory discussion on the present state of Deep Learning in histopathology, we discussed Histopathology, Digital Histopathology, the possibilities of Machine Learning in the area, the various applications, followed by a detailed discussion of the challenges involved in working with Digital Microscopic Slide Images and in the application of Deep Learning Algorithms to them. In this blog, we shall be discussing in greater detail the applicability of Deep Learning to Histopathology from a methodological perspective along with the tasks it helps accomplish using relevant work for illustration. The applicability of deep learning can be studied in terms of the tasks it performs or in terms of the learning paradigm, which is the classification we shall be using in this writeup. The different learning algorithms, viz a viz Deep Learning for histopathology, along with the tasks are visualized in the following overview.