## 2 Methodology
### Proposed Model Orga-Dete
#### 2.2.1 Bi-Directional Feature Pyramid Network  
Earlier versions, such as YOLOv3 [29], employed the conventional feature pyramid network (FPN) [30] to fuse multi-scale features using a top-down pathway. However, conflicts between shallow spatial features and deep semantic representations led to inefficient cross-layer information flow, especially during large object detection. This necessitated repeated downsampling and upsampling operations, which increased computational overhead and compromised accuracy. To overcome these limitations, later models--including YOLOv5 [31], YOLOv8, and YOLOv11--adopted the Path Aggregation Network (PANet) [32] as a complement to the FPN. The resulting FPN + PAN architecture introduces a bottom-up pathway in addition to the original top-down design, allowing low-level localization features to be transmitted directly through lateral connections. This structure enables complementary fusion of semantic and spatial information, as illustrated in Figure 2a,b. Despite the bidirectional feature propagation enabled by FPN + PAN, its reliance on a fixed-weight summation mechanism limits its adaptability. This inflexibil  
Figure 1: Network architecture diagram of YOLOv11.  
ity hinders the effective balancing of spatial detail and semantic abstraction, ultimately weakening the model's ability to distinguish targets from background noise.  
To address these issues--particularly the defocus-induced background noise common in lung organoid microscopy--we employ the bi-directional feature pyramid network (BiFPN) [33], shown in Figure 2c, for superior multi-scale feature fusion and spatial-semantic alignment.  
BiFPN introduces a novel cross-scale, bidirectional architecture that unifies top-down and bottom-up pathways into a single cohesive structure. Unlike FPN + PAN, which processes each direction independently, BiFPN integrates both into a single network layer, allowing for progressive feature refinement across multiple stacked layers. Each stacking iteration functions as a recalibration cycle, progressively improving target localization. Structurally, BiFPN reduces parameter overhead by eliminating nodes that receive only unidirectional inputs, thus maintaining efficient cross-scale connectivity while minimizing computation. These innovations enable BiFPN to deliver more effective fusion of low-level spatial cues (e.g., organoid boundaries) with high-level semantic context (e.g., cell type classification), achieving a critical balance between accuracy and efficiency in feature pyramid design.  
BiFPN introduces a fast normalized fusion mechanism that dynamically evaluates and weights the contributions of multi-scale features using learnable parameters. The core principle is illustrated in Equation (1):  
\[O=\sum_{i}\frac{\omega_{i}}{\epsilon+\frac{\sum_{j}}{j}\omega_{j}}\cdot I_{i} \tag{1}\]  
where \(I_{i}\) denotes the input features, \(O\) represents the output features, \(\omega_{i}\) and \(\omega_{j}\) are learnable scalar weights activated via ReLU to ensure non-negativity, and \(\epsilon=0.0001\) prevents division by zero while maintaining numerical stability. This mechanism prioritizes high-resolution features while suppressing background noise, thereby significantly enhancing recognition accuracy in critical regions.