# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.2 Role of Segmentation Models
\begin{table}
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**Model Name** & **Brief Descriptions** & **Aspects to Address** \\ \hline \multirow{6}{*}{FAM-U-Net [76]} & Integrated Multiscale Feature Extraction (MFE) module and Deep Aggregation Pyramid Pooling Module (DAPPM) to extract the most pertinent features for fluid detection from Optical coherence tomography (OCT) images. Additionally, Convolution Block Attention Module (CBAM) is utilized in the skip connection paths of UNet to enhance the segmentation. & Information related to model parameters or FLOPs are missing. \\ \hline \multirow{6}{*}{MEF-UNet [84]} & Designed a selective feature extraction encoder with detail and structure extraction stages to capture lesion details and shape features accurately. Introduced a context information storage module in skip connections to integrate adjacent feature maps and a multi-scale feature fusion module in the decoder section & Information related to model parameters or FLOPs are missing. \\ \hline \multirow{2}{*}{MISSFormer [87]} & Incorporated local and global context, along with global-local correlation of multi-scale features, into a position-free hierarchical U-shaped transformer architecture. & Incorporating enhanced local context for small RoI could enhance segmentation performance. Information related to model parameters or FLOPs are missing. \\ \hline \multirow{2}{*}{DRD-UNet [91]} & UNet incorporated with pyramidal block with dilated convolution, residual connection and dense layer resulting in a total of 130 layers & The model has high computational complexity (15.40 M) which is greater than baseline UNet on the same dataset by 7.71 M \\ \hline \multirow{2}{*}{Act-AttSegNet [93]} & Incorporated attention gate in SegNet’s skip connection and proposed a Fuzzy Energy-based ACM for vector-valued image segmentation, integrating neural network with ACM by utilizing predicted segmentation masks to remove manual contour initialization & Information related to model parameters or FLOPs are missing. \\ \hline \multirow{2}{*}{Dense-PSP-UNet [124]} & Using Dense-UNet as backbone, incorporated pyramid scene parsing module in the skip connections for extracting multiscale features and contextual associations. & Limited data size may lead to poor generalization of the model, increased risk of overfitting, and compromised performance when applied to larger datasets. \\ \hline \multirow{2}{*}{BowelNet [125]} & Conducted joint localization of five bowel segments: duodenum, jejunum-ileum, colon, sigmoid, and rectum with V-Net, and fine-tuned it using an ensemble multi-task segmentor to leverage meaningful geometric representations. & Segmentation quality can be enhanced by incorporating key points, anatomical information, attention mechanisms, and dilated convolutions. \\ \hline \multirow{2}{*}{SDNet [126]} & Introduced two single-task branches to individually handle teeth and dental plaque segmentation, along with incorporating category-specific features through contrastive and structural constraint module. & The model has high computational and storage complexity due to the presence of two SegNet \\ \hline \multirow{2}{*}{M-Net [127]} & Two SegNet models with distinct pooling strategies, namely max pooling and average pooling, are ensembled and concatenated using a shared softmax classifier. & Information related to model parameters or FLOPs are missing. \\ \hline \multirow{2}{*}{MSFF Net [128]} & Enabled multi-scale feature fusion, spatial feature extraction, channel-wise feature enhancement, refinement of segmentation borders, and focused attention. & Information related to model parameters or FLOPs are missing. \\ \hline \multirow{6}{*}{IAFMSMB Net [92]} & ACM: Active Contour Model; SDNet: Semantic Decomposition Network; PSP: Pyramid Scene Parsing; DRD: Dilation, Residual, and Dense Block FAM-UNet: Multiscale Feature Aggregation and Double-Attention Mixed UNet; MSFF: Multi-Scale Feature Fusion Net; FLOPs: Floating Point Operations; WSI: Whole-Slide Imaging. \\ \cline{1-1}  & & The model struggled to separate small targets with unclear boundaries, reducing segmentation accuracy, especially when dealing with overlapping aggregations. Also, the denoising process often removed small objects, impacting segmentation quality. The multi-step denoising approach and the diffusion model resulted in slow inference times \\ \hline \multirow{2}{*}{CoarseInst [104]} & Weakly supervised framework with box annotations includes coarse mask generation, self-training for instance segmentation, and lightweight encoder with cascade attention block for improved feature information & Information related to model parameters or FLOPs are missing. \\ \hline \hline \end{tabular}
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