To address these questions, the inductive transfer learning of representation learning, a study that skillfully utilizes network-derived features: sequential transfer learning and multi-task learning, has been intensively investigated. In this study, three experiments were conducted to confirm how representation learning using inductive transfer learning affects medical fields: "Using Deep Representation in Pediatric Diagnosis", "Using Deep Representation in Cerebral Hemorrhage Diagnosis" and. As a result, the learning transfer from Model-PedXnets showed improved quantitative performance compared to that from Model-Baseline.
We proposed a supervised multitasking assistance in presentation learning network (SMART-Net) for the diagnosis of intracranial hemorrhage (ICH). Next, transfer learning was performed with a previously trained coder and 3D operator for loudness level tasks. All three studies confirmed that representation learning, which includes sequential transfer learning and multi-task learning, could improve performance, extract semantic information, and make models robust to external data in medical fields.
To convey a better representation, inductive transfer learning that skillfully handles the features extracted from the raw data has been actively studied: sequential transfer learning and multi-task learning. The goal of sequential transfer learning (STL) is to improve learning in the target task by transferring pre-trained knowledge from the source task.
Application of deep representation learning to pediatric diagnosis
To reflect on the actual frequency of occurrence in the medical center, we divided the original dataset into training and validation sets based on the reference date, July 2018. The validation set in the raw original dataset consists of a total of 81,131 radiographs collected over the period from July 2018 to December 2018. To the fixed dataset in the upstream validation set, the same labels with radiographic views were added after a reference date (July 2018), changing the class-wise mean and variance for each label type.
In the upstream tasks, predictions are performed on the target classes with a fully connected layer and a softmax function. Based on the radiography protocol code, they can be divided into the following subclasses: Foot AP, Foot Calcaneus, Foot Hindfoot, Foot Lateral and Foot Oblique in class 68. A fracture occurs mainly in the upper and lower limbs of the body, but anatomically anywhere and often occurs during childhood.
In the fracture classification task, the models have to extract common features of fracture in the upper and lower limbs from radiographs. In the bone age assessment task, mainly hand radiographs are used, and the model must extract detailed features from the bones of the wrist, hand and finger. To validate the Model-PedXnets representation in the upstream and downstream tasks, we have receiver operating characteristic (ROC), the area under the ROC curve (AUC), F1 score (F1), accuracy (ACC), sensitivity ( SEN), used. specificity (SPE), positive predictive values (PPV), and negative predictive values (NPV), mean absolute error (MAE), and mean square error (MSE) for quantitative evaluations [24] of multi-class classification in upstream tasks.
Model-PedXnet-7C was activated in the ROI and the activation maps show that Model-PedXnet-7C can capture clinically meaningful features. In addition, as shown in Figure 5, it can be seen that the intermediate features of the Model-PedXnets are well collected in the t-SNE map by the nested features. Comparisons of activation maps in the intermediate layer of Model-PedXnet, Model-Baseline and Model-ImageNet models using Grad-CAM in the fracture downstream task.
Comparisons of activation maps in the intermediate layer of Model-PedXnet, Model-Baseline and Model-ImageNet using channel-wise average activation map in the BAA. The sample in the first row is 152 months old and the sample in the second row is 167 months old. Second, as shown in Figure 2, we performed excessive random undersampling in the raw original dataset to construct class-balanced datasets according to the anatomical hierarchy of radiographic views.
In the upstream task, we designed the class-balanced pediatric X-ray datasets (PedXnets) using radiographic display labels and performed representation learning for pediatric problems through a radiographic classification task on PedXnets in a supervised manner. In addition, the proposed representational learning allowed the network to capture more semantic features in the ROI of X-ray images.
Application of deep representation learning to brain hemorrhage diagnosis
- Background and Objective
- Materials and Methods
- Experiments and Results
- Discussion
- Summary
Upstream, the split-weight model encoder is trained as a robust feature extractor that captures global features by performing slice-level multi-pronunciation tasks (classification, segmentation, and reconstruction). Subsequently, transfer learning was performed with a previously trained encoder and a 3D operator (classifier or segmenter) for loudness level tasks. Using four test sets (one internal and two external test sets reflecting the natural occurrence of ICH, and one public test set with a relatively small volume of ICH cases), we compared our SMART-Net at the volume level with the state-of-the-art ICH classification methods, ICH segmentation methods and representation learning methods.
In downstream tasks, there are two types of target tasks at the volume level; classification and segmentation of ICH respectively (see Figure 10-(b, c)). A downstream task is a process of extending to volume-level ICH tasks by transferring learning using the pre-trained encoder in the upstream. We tested our SMART-Net against existing, state-of-the-art models in volume-level ICH classification and segmentation, using four test sets to assess the models' robustness and performance (see Table 6).
Comparisons of the pretrained encoder activation map in the upstream task according to multiple pretense task combinations and the previous representation learning approaches. Illustrations of the output mismatches caused by the target-specific multihead structure and by the effects of the consistency loss in the upstream task. The second and fifth columns are the activation maps of the segmentation head according to the consistency loss.
The activation map of the classification head according to the consistency loss is shown in the third and sixth columns. As a result, SMART-Net-SEG achieved the best DSC and FPV at 0.57 and 4.5μL, respectively, based on the average result of the four test sets. Quantitative results of target classification and volume-level segmentation tasks for a comparative analysis with previous methods on four test sets.
Comparisons of false-positive reduction performance on the normal cases in volume level segmentation using boxplots on four test sets. The quantitative results of pretext task ablation studies in volume level classification and segmentation tasks are presented in Table 7. Quantitative results of volume level target classification and segmentation tasks for a comparative analysis with ablation studies on four test sets.
SMART-Net-CLS and SMART-Net-SEG had the best classification and segmentation results in four test sets. We presented a multi-task representation learning network for ICH volume level classification and segmentation in NCCT.
Application of deep representation learning to low-dose CT denoising task
Compared to existing approaches, our framework has achieved state-of-the-art volume-level results for both ICH classification and segmentation performance, as indicated by the evaluation results. The head and neck noise reduction dataset was collected by scanning the database of Asan Medical Center, Republic of Korea, for patients who underwent consecutive CT scans between July 2020 and August 2020. Taking into account the virtues of the Fourier domain, we included the FFT scan Conv Block our generator to improve performance.
In the generator, we adopted RED-CNN as the basic denoiser and added an FFT-Conv block at each layer for better denoising performance. The first target-specific layer for REC guides the discriminator to the reconstruction task, thereby improving the discrimination and generalization capabilities of the classifier module. Due to the nature of the medical image, the background and bone regions with the same LDCT and NDCT occupy a significant amount of the CT image, which causes considerable uncertainty when the discriminator makes a decision.
𝐿𝐶𝑜𝑛𝑠𝑖𝑠𝑡.=𝔼𝑥𝑫[‖𝑫𝐶𝐿𝑆(𝑥𝑫) − 𝑫𝐶𝐅𝑫𝑶( ))‖ + ‖𝑫𝑆𝐸𝐺(𝑥) − 𝑫𝑆𝐸𝑫𝐺(𝑫𝑅𝐸𝐶(𝑥𝑫)))‖], (4) where 𝐫𝐶𝐸𝐶(𝑥𝑫)) is the input and 𝑫𝐶𝐸𝐶(𝑥𝑫)) is the input for CLS. -, SEG and REC layers of multi-task discriminator scans and normal-dose CT scans. We chose the RED-CNN [65] base denoiser with 10(de) convolutional layers at both the encoder and the decoder to demonstrate the influence of the multitask-based discriminators. We clipped the foreground of the CT image and then randomly extracted eight 64×64 patches from each 512×512 original image in each epoch.
We used the projecting conflicting gradients (PCGrad) technique [76], which projects the gradient of one task onto the normal plane of the gradient of any other activity with a conflicting gradient, to eliminate gradient interference between multiple tasks. To study the effect of the different MTD-GAN components, we performed excessive ablation tests. In reality, the radiologist mistook the outputs of the model with the highest FID score performance for real LDCT images.
This is, to our knowledge, the first study to use three vision multitasks for the discriminator in the LDCT denoising task. In the second study, multi-task learning was applied to develop a robust feature extractor for the brain hemorrhage identification task, resulting in improved performance even when using external data. In the third study, multi-task learning was used to improve the discriminator for low-dose CT denoise tasks, thereby
Iannaccone, G.: WW Greulich and SI Pyle: Radiographic atlas of the skeletal development of the hand and wrist. Valentin, J., Boice Jr, J., Clarke, R., Cousins, C., Gonzalez, A., Lee, J., Lindell, B., Meinhold, C., Mettler Jr, F., Pan, Z .: Published on behalf of the International Commission on Radiological Protection.
