  Melanoma is an aggressive neoplasm responsible for the majority of deaths
from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most
challenging melanocytic lesions due to their ambiguous morphological features.
The gold standard for its diagnosis and prognosis is the analysis of skin
biopsies. In this process, dermatopathologists visualize skin histology slides
under a microscope, in a high time-consuming and subjective task. In the last
years, computer-aided diagnosis (CAD) systems have emerged as a promising tool
that could support pathologists in daily clinical practice. Nevertheless, no
automatic CAD systems have yet been proposed for the analysis of spitzoid
lesions. Regarding common melanoma, no proposed system allows both the
selection of the tumoral region and the prediction of the diagnosis as benign
or malignant. Motivated by this, we propose a novel end-to-end
weakly-supervised deep learning model, based on inductive transfer learning
with an improved convolutional neural network (CNN) to refine the embedding
features of the latent space. The framework is composed of a source model in
charge of finding the tumor patch-level patterns, and a target model focuses on
the specific diagnosis of a biopsy. The latter retrains the backbone of the
source model through a multiple instance learning workflow to obtain the
biopsy-level scoring. To evaluate the performance of the proposed methods, we
perform extensive experiments on a private skin database with spitzoid lesions.
Test results reach an accuracy of 0.9231 and 0.80 for the source and the target
models, respectively. Besides, the heat map findings are directly in line with
the clinicians' medical decision and even highlight, in some cases, patterns of
interest that were overlooked by the pathologist due to the huge workload.
