Expert Branches: Module Diversity for Stronger Feature Learning in Laparoscopic Segmentation

Module diversity fundamentally enhances a model’s ability to learn geometric structure by enabling a broader and more expressive set of feature representations. While many architectures improve performance by scaling parameters or relying on large-scale pretraining, these strategies make it difficult to identify which design principles truly enhance feature learning capability, especially in challenging domains with limited data such as laparoscopic surgical segmentation. This work investigates a parameter-constrained, no-pretraining setting to isolate the intrinsic feature learning capability of different module configurations. We introduce expert branches, a design concept that assigns different module families to their own independent pathways rather than mixing all features within a single stream. This separation encourages branch-specific specialization (Experts), reduces parameters, and avoids the entanglement that commonly obscures each module’s contribution. We test this idea with TriEB, a UNet-based model incorporating CNN, deformable-convolution, and dynamic-snake branches with less total parameters. TriEB surpasses the vanilla UNet, the non-diverse TriCNN counterpart, and transformer-based models including SegFormer and Swin on the DSAD laparoscopic dataset. These results demonstrate that expert branches offer a more effective design principle for extracting diverse features from surgical imagery. The study highlights module diversity as a promising, architecture-agnostic framework for building efficient, interpretable, and data-adaptive feature extractors.