Parkinson's disease is easy to diagnose when it is advanced, but it is very
difficult to diagnose in its early stages. Early diagnosis is essential to be
able to treat the symptoms. It impacts on daily activities and reduces the
quality of life of both the patients and their families and it is also the
second most prevalent neurodegenerative disorder after Alzheimer in people over
the age of 60. Most current studies on the prediction of Parkinson's severity
are carried out in advanced stages of the disease. In this work, the study
analyzes a set of variables that can be easily extracted from voice analysis,
making it a very non-intrusive technique. In this paper, a method based on
different deep learning techniques is proposed with two purposes. On the one
hand, to find out if a person has severe or non-severe Parkinson's disease, and
on the other hand, to determine by means of regression techniques the degree of
evolution of the disease in a given patient. The UPDRS (Unified Parkinson's
Disease Rating Scale) has been used by taking into account both the motor and
total labels, and the best results have been obtained using a mixed multi-layer
perceptron (MLP) that classifies and regresses at the same time and the most
important features of the data obtained are taken as input, using an
autoencoder. A success rate of 99.15% has been achieved in the problem of
predicting whether a person suffers from severe Parkinson's disease or
non-severe Parkinson's disease. In the degree of disease involvement prediction
problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a
full deep learning pipeline for data preprocessing and classification has
proven to be very promising in the field Parkinson's outperforming the
state-of-the-art proposals.

该论文提出了一种基于深度学习技术的方法，通过语音分析及回归技术能够判断一个人是否患有严重的帕金森症，并预测病情的程度，取得了非常有前景的效果。

使用多任务神经网络确定帕金森病患者的严重程度

Determining the severity of Parkinson's disease in patients using a  multi task neural network

Accurate brain tumor segmentation in the early stages of the disease is
crucial for the treatment's effectiveness, avoiding exhaustive visual
inspection of a qualified specialist on 3D MR brain images of multiple
protocols (e.g., T1, T2, T2-FLAIR, T1-Gd). Several networks exist for Glioma
segmentation, being nnU-Net one of the best. In this work, we evaluate
self-calibrated convolutions in different parts of the nnU-Net network to
demonstrate that self-calibrated modules in skip connections can significantly
improve the enhanced-tumor and tumor-core segmentation accuracy while
preserving the wholetumor segmentation accuracy.

早期疾病准确的脑肿瘤分割对治疗有效性至关重要，避免了对多种协议（如 T1、T2、T2-FLAIR、T1-Gd）的 3D MR 脑图像进行繁琐的专家目视检查。本文评估 nnU-Net 网络中自校准卷积在不同部分的应用，证明了在跳跃连接中使用自校准模块可以显著提高增强肿瘤和肿瘤核的分割准确性，同时保持整个肿瘤的分割准确性。