1. A computer-implemented method for locating one or more anomalies on a medical sample from an image thereof, the medical sample and the image being prepared under a setting comprising one or more setting-based variables, the method comprising: locating, from the image, any object-of-interest suspected to be anomalous on the medical sample; and when one or more objects-of-interest are located, determining whether an individual object-of-interest is an individual anomaly by an anomaly-detection process and repeating the anomaly-detection process for each of the one or more objects-of-interest; wherein the anomaly-detection process comprises: using plural base classifiers individually to classify the individual object-of-interest, wherein each base classifier respectively extracts features of the individual object-of-interest and generates, according to the extracted features, a score indicating a likelihood of the individual object-of-interest being anomalous; and using an aggregate classifier to combine the scores generated by the base classifiers to determine whether the individual object-of-interest is the individual anomaly, wherein the aggregate classifier determines a dependability measure of an individual score for each base classifier according to the one or more setting-based variables, and selectively combines the scores of all the base classifiers according to the dependability measures of the scores.