Segmentation models for brain lesions in MRI are commonly developed for a
specific disease and trained on data with a predefined set of MRI modalities.
Each such model cannot segment the disease using data with a different set of
MRI modalities, nor can it segment any other type of disease. Moreover, this
training paradigm does not allow a model to benefit from learning from
heterogeneous databases that may contain scans and segmentation labels for
different types of brain pathologies and diverse sets of MRI modalities. Is it
feasible to use Federated Learning (FL) for training a single model on client
databases that contain scans and labels of different brain pathologies and
diverse sets of MRI modalities? We demonstrate promising results by combining
appropriate, simple, and practical modifications to the model and training
strategy: Designing a model with input channels that cover the whole set of
modalities available across clients, training with random modality drop, and
exploring the effects of feature normalization methods. Evaluation on 7 brain
MRI databases with 5 different diseases shows that such FL framework can train
a single model that is shown to be very promising in segmenting all disease
types seen during training. Importantly, it is able to segment these diseases
in new databases that contain sets of modalities different from those in
training clients. These results demonstrate, for the first time, feasibility
and effectiveness of using FL to train a single segmentation model on
decentralised data with diverse brain diseases and MRI modalities, a necessary
step towards leveraging heterogeneous real-world databases. Code will be made
available at: this https URL

使用分割模型对脑部病变进行 MRI 分割的研究表明，采用联邦学习的方法可以训练一个单一的模型，该模型能够在包含不同脑部病理和 MRI 模态的分布式数据库中进行有效分割，具有可行性和有效性。

不同脑疾病和 MRI 模态下的客户数据库联邦学习的可行性

Feasibility of Federated Learning from Client Databases with Different  Brain Diseases and MRI Modalities

Models for segmentation of brain lesions in multi-modal MRI are commonly
trained for a specific pathology using a single database with a predefined set
of MRI modalities, determined by a protocol for the specific disease. This work
explores the following open questions: Is it feasible to train a model using
multiple databases that contain varying sets of MRI modalities and annotations
for different brain pathologies? Will this joint learning benefit performance
on the sets of modalities and pathologies available during training? Will it
enable analysis of new databases with different sets of modalities and
pathologies? We develop and compare different methods and show that promising
results can be achieved with appropriate, simple and practical alterations to
the model and training framework. We experiment with 7 databases containing 5
types of brain pathologies and different sets of MRI modalities. Results
demonstrate, for the first time, that joint training on multi-modal MRI
databases with different brain pathologies and sets of modalities is feasible
and offers practical benefits. It enables a single model to segment pathologies
encountered during training in diverse sets of modalities, while facilitating
segmentation of new types of pathologies such as via follow-up fine-tuning. The
insights this study provides into the potential and limitations of this
paradigm should prove useful for guiding future advances in the direction. Code
and pretrained models: this https URL

通过联合训练多模态 MRI 数据库的不同脑病理学和模态集合，能够实现对训练过程中遇到的病理分割和新型病理的分割，并且提供实际的好处，该方法为今后的研究方向提供了有用的指导。

基于不同脑部疾病和模态的 MRI 数据库联合学习的可行性和益处

Feasibility and benefits of joint learning from MRI databases with  different brain diseases and modalities for segmentation

Stroke is the second leading cause of mortality worldwide. Immediate
attention and diagnosis play a crucial role regarding patient prognosis. The
key to diagnosis consists in localizing and delineating brain lesions. Standard
stroke examination protocols include the initial evaluation from a non-contrast
CT scan to discriminate between hemorrhage and ischemia. However, non-contrast
CTs may lack sensitivity in detecting subtle ischemic changes in the acute
phase. As a result, complementary diffusion-weighted MRI studies are captured
to provide valuable insights, allowing to recover and quantify stroke lesions.
This work introduced APIS, the first paired public dataset with NCCT and ADC
studies of acute ischemic stroke patients. APIS was presented as a challenge at
the 20th IEEE International Symposium on Biomedical Imaging 2023, where
researchers were invited to propose new computational strategies that leverage
paired data and deal with lesion segmentation over CT sequences. Despite all
the teams employing specialized deep learning tools, the results suggest that
the ischemic stroke segmentation task from NCCT remains challenging. The
annotated dataset remains accessible to the public upon registration, inviting
the scientific community to deal with stroke characterization from NCCT but
guided with paired DWI information.

中风是全球第二大死因，本研究介绍了 APIS 数据集，它包含了 NCCT 和 ADC 研究的急性缺血性中风患者，可用于帮助诊断、定位和分析中风病变。

APIS: 一个用于脑缺血分割挑战的 CT-MRI 成对数据集

APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge

Reducing the requirement for densely annotated masks in medical image
segmentation is important due to cost constraints. In this paper, we consider
the problem of inferring pixel-level predictions of brain lesions by only using
image-level labels for training. By leveraging recent advances in generative
diffusion probabilistic models (DPM), we synthesize counterfactuals of "How
would a patient appear if X pathology was not present?". The difference image
between the observed patient state and the healthy counterfactual can be used
for inferring the location of pathology. We generate counterfactuals that
correspond to the minimal change of the input such that it is transformed to
healthy domain. This requires training with healthy and unhealthy data in DPMs.
We improve on previous counterfactual DPMs by manipulating the generation
process with implicit guidance along with attention conditioning instead of
using classifiers. Code is available at this https URL.

本文提出一种使用生成扩散概率模型生成反事实图来推断脑病变像素级预测的方法，并且通过隐含指导和注意力调节来操作生成过程，从而提升了之前的方法。