## 4 Discussion and Conclusions  
Organoids--three-dimensional in vitro cultures that closely mimic the structural and functional characteristics of human organs--are emerging as essential tools for disease modeling and drug discovery. However, traditional microscopic evaluation methods are hindered by subjectivity and low throughput. Deep learning approaches address these limitations by enabling automated, quantitative analysis of organoid morphology, improving both detection accuracy and processing efficiency. To meet the challenges of detecting dense, multi-scale organoids with low-contrast morphologies, this study introduces Orga-Dete, a lightweight detection model built upon YOLOv11n and optimized through three architectural innovations: BiFPN replaces the original FPN + PAN to enable bidirectional multi-scale feature fusion without increasing computational overhead. MPCA, inserted after C2 PSA, enhances micro-organoid feature representation by combining coordinate attention with a multi-path structure. EMASlide-Loss, a dynamic threshold calibration loss, mitigates class imbalance and reduces the omission of hard samples. Under the dataset and experimental settings of this study, compared to YOLO variants, Faster R-CNN, and Transformer-based RTDETR-r18, Orga-Dete demonstrates superior accuracy, computational efficiency, and parameter compactness, though its generalizability remains to be validated on larger-scale cross-scenario data. The practical value of Orga-Dete lies in its ability to perform high-speed, non-invasive morphological analysis of organoids from microscopic images--achieving millisecond-level parsing while preserving sample viability. Its parallel processing capability reduces contamination risks and overcomes throughput limitations associated with manual inspection. This enables standardized, high-throughput analysis frameworks for drug toxicity screening and dynamic efficacy evaluation, accelerating the shift from 2D cell assays to 3D tissue-based platforms in pharmaceutical research. \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Detection Model** & **[EMAIL_ADDRESS]** \\ \hline \multirow{4}{*}{Dataset (b)} & YOLOv8n & 91.3 \(\pm\) 0.4\% \\  & YOLOv11n & 91.6 \(\pm\) 0.3\% \\  & RTDETR-r18 & 90.1 \(\pm\) 0.2\% \\  & YOLOv12n & 91.7 \(\pm\) 0.3\% \\  & Ours & 92.5 \(\pm\) 0.3\% \\ \hline \multirow{4}{*}{Dataset (c)} & YOLOv8n & 84.6 \(\pm\) 0.2\% \\  & YOLOv11n & 83.5 \(\pm\) 0.2\% \\ \cline{1-1}  & RTDETR-r18 & 83.2 \(\pm\) 0.1\% \\ \cline{1-1}  & YOLOv12n & 82.3 \(\pm\) 0.3\% \\ \cline{1-1}  & Ours & 84.4 \(\pm\) 0.3\% \\ \hline \end{tabular}
\end{table} 
Table 12: Performance on other organoid datasets. Figure 9: Representative images of organoid datasets: (**a**) lung organoids, (**b**) duodenal biopsy organoid dataset, and (**c**) murine intestinal organoid dataset. Nevertheless, this study acknowledges two key limitations. First, regarding data imbalance, the class distribution skew constrains Orga-Dete's performance. Despite partially compensating for these issues, inter-class accuracy gaps remain significant, indicating the need for more balanced and comprehensive datasets. Second, concerning reduced performance in multi-class tasks with morphological heterogeneity, Orga-Dete underperforms slightly on the murine intestinal organoid dataset compared to state-of-the-art models, highlighting limitations in generalization to morphologically diverse targets. X.H. : investigation, literature search, writing--original draft preparation. Q.G., H.Z. and F.M. : writing--review and editing. D.L. : investigation, supervision. G.L. : project administration, funding acquisition. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript. This work was supported by the Suzhou Key Technology (Research) Project of Critical and Infectious Diseases Precaution and Control (Grant No. GWZX202102) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA16021102).