# 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}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model Name** & **Brief Descriptions** & **Aspects to Address** \\ \hline \multirow{6}{*}{M-YOLACT++ [105]} & Utilized ResNet-101 as the backbone network, followed by a multi-scale feature fusion (MSFF) module with cris-cross attention and convolutional block attention modules to aggregate contextual information from feature maps across all scales. & The absence of information regarding model parameters or FLOPs is noted. Incorporating prospective data could provide further insights into the model’s generalizability. \\ \hline \multirow{3}{*}{SibNet [129]} & Utilized seed map to reduce overlap between instances, aiding counting and generating an instance segmentation map that accurately depicts individual instances’ arbitrary shapes and sizes, even under occlusion. & The model can be extended to count fractional items and has been considered for homogeneous food counting, where each item corresponds to one food, preventing the identification of multi-dish foods. \\ \hline \multirow{3}{*}{SPR-Mask R-CNN [130]} & Combined ResNet101 with Feature Pyramid Network (FPN) to extract a multi-scale feature map, along with RoAlign method for processing features at different scales. & Improvement is required for small oran identification such as endohyroid vessel. It cann not differentiate anatomically symmetrical structures such as left and right thyroid lobes. \\ \hline \multirow{3}{*}{MSS-WISN [131]} & Incorporated a feature extraction network to enhance feature expression and a feature fusion network to emphasize salient features, mitigating the impact of scale variations. & Information related to model parameters or FLOPs are missing. \\ \hline \multirow{3}{*}{DSCA-Net [132]} & Incorporated hierarchical feature extraction module in the encoder and feature attention mechanism in the decoder network and deep scale feature fusion in both encoder and decoder. & The model has higher computational complexity (32.50 M) in comparison to ResUNet++ (20.42 M). \\ \hline \multirow{3}{*}{FoodMask [133]} & Utilized FPN as a backbone for feature extraction, integrating clustering concepts for food instance counting, segmentation, and recognition. & The food counting technique is not class-agnostic, posing challenges for its application across a wide variety of foods. \\ \hline \multirow{6}{*}{PanoforTeeth [80]} & M-YOLACT++: Modified YOLACT++; MSS-WISN: Multi-Scale and Multi-Staining White Blood Cell Instance Segmentation Network; IAFMSMB Net: Instance-Aware Filters and Multi-Scale Mask Branch; DSCA-Net: Double-Stage Codce Attention Network. \\ \cline{1-1} \cline{2-2}  & **Rief Descriptions** & **Aspects to Address** \\ \hline \multirow{3}{*}{Hybrid-PA-Net [74]} & Panoptic segmentation head integrates pixel-wise classification from CCNN-ResNet50 with instance output of PANet. & Incorporating prospective data could provide further insights into the model’s generalization. \\ \cline{1-1} \cline{2-2}  & Dual-path transformer block integrates various attention mechanisms including pixel-to-memory feedback attention, pixel-to-pixel self-attention, and memory-to-pixel and memory-to-memory self-attention. It also incorporated a stacked decoder for aggregating multi-scale features across various decoding resolutions. & The model is computationally expensive and lacks generalization as it is trained on a single-center dataset. \\ \hline \multirow{3}{*}{ms-SP Net [81]} & Utilized ms-SP along with connection between multi-scale features and spatial RoI characteristics & The model is parametric. \\ \hline \multirow{6}{*}{VertONNet [83]} & Combined UNet and MaskRCNN with their own ensemble rule for the generation of unified segmentation results. & The method lacks an end-to-end approach as both UNet and MaskRCNN were trained separately. The ensemble rule was manually crafted, lacking generalization. Effective localization of ‘S1’ and ‘C2’ vertebrae directly influences vertebra localization. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._