1. Method for the automatic analysis of an image ( 1 , 11 , 12 , 13 ) of a biological sample with respect to a pathological relevance, wherein a) local features of the image ( 1 , 11 , 12 , 13 ) are aggregated to a global feature of the image ( 1 , 11 , 12 , 13 ) using a bag of Visual word approach, b) step a) is repeated at least two times using different methods resulting in at least two bag of word feature datasets, c) computation of at least two similarity measures using the bag of word features obtained from a training image dataset and bag of word features from the image ( 1 , 11 , 12 , 13 ) d) the image training dataset comprising a set of Visual words, classifier parameters, including kernel weights and bag of word features from the training images, e) the computation of the at least two similarity measures is subject to an adaptive computation of kernel normalization parameters and/or kernel width parameters, f) for each image ( 1 , 11 , 12 , 13 ) at least one score is computed depending on the classifier parameters and kernel weights, wherein this computation is performed in two steps: at first by computing a dimension-wise score for each dimension of each of the bag of word features for the subimage ( 11 , 12 , 13 ) by using first order Taylor expansions of a support vector machine prediction function around a point x 0 which is a root of the prediction function f(x 0 )=0: f ( x ) = b + ∑ i = 1 S α i y i ∑ m = 1 K β m k m ( x i , x ) ≈ f ( x 0 ) + 〈 x - x 0 , ∇ x f ( x 0 ) 〉 = ∑ m = 1 K ∑ d = 1 V m β m ( f ( x 0 ) V m  β →  1 + ( x - x 0 ) ( d , m ) ∑ i = 1 S α i y i ∂ k m ∂ x ( d ) ( x 0 ) ) = : ∑ m = 1 K ∑ d = 1 V m r d , m ( x ) with r d,m (x) as the relevance score for dimension d of bag of word feature m and input x which is here for notational simplicity a concatenation of all bag of word features instead of a subimage ( 11 , 12 , 13 ), in a second step exploiting the additivity in the bag of word mapping formula and assuming the local feature t belongs to the bag of word feature x, part M(i), with R ( t ) := ∑ d = 1 V M ( t ) r d , M ( t ) ( x ) m d ( t ) defining the relevance for a local feature, being the relevance score of a bag of word dimension weighted with the bag of word contribution of the local feature to the dimension, then the relevance of a pixel is the average of the relevances of all local features which contain the pixel in their support, the support of a local feature is the set of pixels from which that local feature is computed rel ( p ) = ∑ t | p ∈ support ( t ) R ( t ) ∑ t | p ∈ support ( t ) 1 and the at least two similarity measures, the at least one score being a measure of the certainty of one pathological category compared to the image training dataset, g) for each pixel of the image ( 1 , 11 , 12 , 13 ) a pixel-wise score is computed using the classifier parameters, the kernel weights, the at least two similarity measures, the bag of word features of the image ( 1 , 11 , 12 , 13 ), all the local features used in the computation of the bag of word features of the image ( 1 , 11 , 12 , 13 ) and the pixels used in the computations of the local features, h) the pixel-wise score is stored as a heatmap dataset linking the pixels of the image ( 1 , 11 , 12 , 13 ) to the pixel-wise scores.