[已解决]transformer在多模态的应用

Sherrill_L

@FishC
介绍一下transformer在基于多模态数据的计算机辅助诊断方面的应用

最佳答案

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FishC

2023-12-15 11:07:32

在基于多模态数据的计算机辅助诊断中，Transformer模型可以用于处理和融合不同类型的数据，比如图像、文本、声音等。下面以一个示例来介绍Transformer在这方面的应用。

假设我们有一个医学辅助诊断任务，输入数据是一张病人的心电图和相关的文字描述。我们希望通过分析这些数据，预测病人是否患有心脏病。

首先，我们需要将心电图和文字描述转化为模型可以理解的向量表示。对于心电图，我们可以使用卷积神经网络（CNN）来提取特征；对于文字描述，我们可以使用循环神经网络（RNN）或Transformer模型。

接下来，我们使用两个独立的Transformer编码器分别处理心电图和文字描述。每个编码器将输入序列转换为上下文感知的向量序列，并且保持输入序列中元素的相对顺序。这使得我们可以获取到每个模态数据的语义信息。

在得到两个模态数据的向量表示后，我们可以使用Attention机制来融合它们。具体来说，我们可以计算心电图和文字描述之间的注意力权重，然后将它们应用于各自的向量表示上。这样，我们就能够捕捉到模态之间的相关性和重要性。

最后，我们将融合后的向量输入到一个全连接层来进行分类，预测病人是否患有心脏病。

以下是一个简化的示例代码：

1. import torch
2. import torch.nn as nn
3. from torch.nn import TransformerEncoder, TransformerEncoderLayer
5. class MultiModalTransformer(nn.Module):
6.     def __init__(self, d_model, nhead, num_modalities):
7.         super(MultiModalTransformer, self).__init__()
8.         encoder_layers = TransformerEncoderLayer(d_model, nhead)
9.         self.transformer_encoder = TransformerEncoder(encoder_layers, num_modalities)
10.         self.fc = nn.Linear(d_model, 2)  # 2 classes for heart disease prediction
11.    
12.     def forward(self, electrocardiogram, text_description):
13.         # Encoder for electrocardiogram sequence
14.         encoding_electrocardiogram = self.transformer_encoder(electrocardiogram)
15.         
16.         # Encoder for text description sequence
17.         encoding_text_description = self.transformer_encoder(text_description)
18.         
19.         # Attention mechanism
20.         attention_weights = torch.matmul(encoding_electrocardiogram, encoding_text_description.transpose(-2, -1))
21.         attention_electrocardiogram = torch.matmul(attention_weights, encoding_text_description)
22.         attention_text_description = torch.matmul(attention_weights.transpose(-2, -1), encoding_electrocardiogram)
23.         
24.         # Concatenate the modalities and feed into fully connected layer for classification
25.         output = self.fc(torch.cat([attention_electrocardiogram, attention_text_description], dim=-1))
26.         return output

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注意，上面的代码只是一个示例，实际应用中需要根据具体任务的需求进行调整和优化。

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Sherrill_L

FishC 发表于 2023-12-15 11:07
在基于多模态数据的计算机辅助诊断中，Transformer模型可以用于处理和融合不同类型的数据，比如图像、文本 ...

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Sherrill_L

FishC 发表于 2023-12-15 11:07
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Sherrill_L

FishC 发表于 2023-12-15 15:11
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Sherrill_L

解释一下High-Resolution Image Synthesis with Latent Diffusion Models这篇论文的原理
@FishC

Sherrill_L

FishC 发表于 2023-12-20 13:05
嗨！"High-Resolution Image Synthesis with Latent Diffusion Models" 这篇论文主要探讨了一种利用潜在 ...

classifer-free guidance是什么，怎么获得的

Sherrill_L

@FishC 改写这段话：
在理论研究方面，学校图书馆订阅了大量的电子刊物全文数据库，如ACM、IEEE、Springer、Wiley等，可以方便地获取国内外最新的文献资料，为本项目的理论研究提供充分的科研资料。以上高性能计算资源和科研资料为本项目的顺利实施提供了有力的前提保障。

Sherrill_L

@FishC write a reply to this comment:
"In all the experiments, 80% of the brain MRI slices are used for training, and the remainder part serves as the testing set."
This is a clear sign of a slice-level train-test split, an approach giving misleading outcomes due to the presence of data leakage between the two sets. The fundamental assumption for cross-validation or train/test split validity is the independence of the generated sets, an assumption severely compromised when dealing with slices coming from MRI volumes from individual patients. In such cases, slices extracted from the same volume display high correlation, violating the independence assumption. Consequently, a single slice may be allocated to the training set, while a nearly identical neighbouring slice may end up in the test set. This means that a high accuracy can be apparently attained even with no significant information about AD being extracted by the algorithm, but rather simply because for each test slice there is likely to be an almost identical copy that the network has seen during training.
Several recent publications have highlighted this issue (for example [1] and [2]). In particular, in [2] the authors demonstrated that incorrect slice-level splitting can inflate results by over 40%. For instance, employing random labelling of patients in an OASIS-derived dataset and implementing slice-level splits resulted in a misleading 96% accuracy, whereas subject-level split accuracy was 50%, as expected from a randomized experiment with two classes. To address this issue appropriately, it is crucial to ensure that all slices from an individual patient are exclusively allocated to either the training or test set - a practice known as subject-level split.

Sherrill_L

@FishC Reply to the comment without additional experiment:

Given the comments above, I believe the paper has major flaws that make it unsuitable for publication. The concern about the slice-level vs subject-level split means that the results have little practical value for AD classification. It looks like an interesting machine learning exercise that uses tha data without really understanding them, automatically making assumptions (like IID data) that clearly do not hold in practice.
Incidentally, reading [3] (rather than treating the data as a "Kaggle competition") one learns that the scoring of the level of dementia is based on the CDR scale, specifically "the CDR is a dementia-staging instrument that rates subjects for impairment [...] based on the collateral source and subject interview". As one can easily gather, asking and observing people is an intrinsically noisy process and therefore a certain level of "teacher noise" should be expected, i.e. a 99% accuracy is unrealistic. One should expect a much higher error rate, especially between neighbouring classes; in other words while one could somewhat believe a 9x% accuracy between no-dementia and moderate-dementia, it's highly unlikely that the same level can be achieved between mild- and very-mild-dementia and this should have prompted the authors to double-check their results.

Sherrill_L

@FishC rephrase it:
The Internet of Things (IoT) is a system generally
comprising a large number of distributed mobile
terminals embedded with sensors. IoT applications
are typically found in several felds, such as sports,
agriculture, commerce, households, and medical
care. In recent years, IoT has developed rapidly as
it can minimize the effort with high accuracy, and
effectively reduce time by predicting the outlook
in a better way. Integrating IoT devices with actuators and sensors into gadget-like appliances and
machines can introduce automation to almost
every feld.

Sherrill_L

@FishC rephrase it:
Healthcare is one of the most attractive application areas for IoT. IoT technology has great
potential for remote medical care (also named
telemedicine or eHealth). It can effectively help
doctors solve many problems caused by distance
barriers. Sensor devices in IoT can bring great convenience to telemedicine and have recently been
developed for daily monitoring of physical activity awareness, personal health, and medical care.
The smart wearable sensor devices used in remote
health monitoring systems [1] collect and store a
large amount of patient health status data (e.g.,
body temperature, blood pressure, and electromyography) through sensors. Afterward, doctors can
remotely monitor the health of patients in real time
and make appropriate treatment recommendations
accordingly until the patients have fully recovered.
Hence, IoT technology is helpful to achieve early
diagnosis of patients and thus improve their health.

Sherrill_L

@FishC rephrase it in English:

Healthcare is one of the most attractive application areas for IoT. IoT technology has great
potential for remote medical care (also named
telemedicine or eHealth). It can effectively help
doctors solve many problems caused by distance
barriers. Sensor devices in IoT can bring great convenience to telemedicine and have recently been
developed for daily monitoring of physical activity awareness, personal health, and medical care.
The smart wearable sensor devices used in remote
health monitoring systems [1] collect and store a
large amount of patient health status data (e.g.,
body temperature, blood pressure, and electromyography) through sensors. Afterward, doctors can
remotely monitor the health of patients in real time
and make appropriate treatment recommendations
accordingly until the patients have fully recovered.
Hence, IoT technology is helpful to achieve early
diagnosis of patients and thus improve their health.