  Forensic pathology is critical in analyzing death manner and time from the
microscopic aspect to assist in the establishment of reliable factual bases for
criminal investigation. In practice, even the manual differentiation between
different postmortem organ tissues is challenging and relies on expertise,
considering that changes like putrefaction and autolysis could significantly
change typical histopathological appearance. Developing AI-based computational
pathology techniques to assist forensic pathologists is practically meaningful,
which requires reliable discriminative representation learning to capture
tissues' fine-grained postmortem patterns. To this end, we propose a framework
called FPath, in which a dedicated self-supervised contrastive learning
strategy and a context-aware multiple-instance learning (MIL) block are
designed to learn discriminative representations from postmortem
histopathological images acquired at varying magnification scales. Our
self-supervised learning step leverages multiple complementary contrastive
losses and regularization terms to train a double-tier backbone for
fine-grained and informative patch/instance embedding. Thereafter, the
context-aware MIL adaptively distills from the local instances a holistic
bag/image-level representation for the recognition task. On a large-scale
database of $19,607$ experimental rat postmortem images and $3,378$ real-world
human decedent images, our FPath led to state-of-the-art accuracy and promising
cross-domain generalization in recognizing seven different postmortem tissues.
The source code will be released on
\href{https://github.com/ladderlab-xjtu/forensic_pathology}{https://github.com/ladderlab-xjtu/forensic\_pathology}.
