108,958 research outputs found
A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.
We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing 'slide-level' diagnosis by using individual 'cell-level' diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
The pandemic resulted in vast repositories of unstructured data, including
radiology reports, due to increased medical examinations. Previous research on
automated diagnosis of COVID-19 primarily focuses on X-ray images, despite
their lower precision compared to computed tomography (CT) scans. In this work,
we leverage unstructured data from a hospital and harness the fine-grained
details offered by CT scans to perform zero-shot multi-label classification
based on contrastive visual language learning. In collaboration with human
experts, we investigate the effectiveness of multiple zero-shot models that aid
radiologists in detecting pulmonary embolisms and identifying intricate lung
details like ground glass opacities and consolidations. Our empirical analysis
provides an overview of the possible solutions to target such fine-grained
tasks, so far overlooked in the medical multimodal pretraining literature. Our
investigation promises future advancements in the medical image analysis
community by addressing some challenges associated with unstructured data and
fine-grained multi-label classification.Comment: Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV) Workshops 202
- …