  Accurate classification of laryngeal vascular as benign or malignant is
crucial for early detection of laryngeal cancer. However, organizations with
limited access to laryngeal vascular images face challenges due to the lack of
large and homogeneous public datasets for effective learning. Distinguished
from the most familiar works, which directly transfer the ImageNet pre-trained
models to the target domain for fine-tuning, this work pioneers exploring
two-step heterogeneous transfer learning (THTL) for laryngeal lesion
classification with nine deep-learning models, utilizing the diabetic
retinopathy color fundus images, semantically non-identical yet vascular
images, as the intermediate domain. Attention visualization technique, Layer
Class Activate Map (LayerCAM), reveals a novel finding that yet the
intermediate and the target domain both reflect vascular structure to a certain
extent, the prevalent radial vascular pattern in the intermediate domain
prevents learning the features of twisted and tangled vessels that distinguish
the malignant class in the target domain, summarizes a vital rule for laryngeal
lesion classification using THTL. To address this, we introduce an enhanced
fine-tuning strategy in THTL called Step-Wise Fine-Tuning (SWFT) and apply it
to the ResNet models. SWFT progressively refines model performance by
accumulating fine-tuning layers from back to front, guided by the visualization
results of LayerCAM. Comparison with the original THTL approach shows
significant improvements. For ResNet18, the accuracy and malignant recall
increases by 26.1% and 79.8%, respectively, while for ResNet50, these
indicators improve by 20.4% and 62.2%, respectively.
