A method for improving lung cancer screening and detection from a computed tomography data set obtained for a patient , comprising the steps of: a) supplying the data set to a global predictive model comprising a three-dimensional deep convolutional neural network trained to predict at least the probability of the presence of cancer in lung tissue in the data set on a global basis, wherein the global predictive model uses a base feature extractor to identify the presence of lung cancer in the data set , and wherein the global predictive model includes a layer with an attention mechanism; b) supplying the data set to a two-stage prediction model, the two-stage prediction model comprising: 1) a first stage detection model detecting the location of one or more three-dimensional cancer candidates within the data set , wherein the first stage detection model uses the base feature extractor, and 2) a second stage probability model operating on the one or more three-dimensional cancer candidates detected by the first stage detection model and assigning a cancer probability p to each of the three-dimensional cancer candidates ; and c) generating data representing an overall prediction of the probability of cancer in the data set using both the prediction of the global predictive model and the cancer probabilities p assigned by the second stage probability model to each of the three-dimensional cancer candidates , and the location of cancer in the data set , wherein the location of cancer is determined by either the global predictive model or the two-stage prediction model. a) supplying the data set to a global predictive model comprising a three-dimensional deep convolutional neural network trained to predict at least the probability of the presence of cancer in lung tissue in the data set on a global basis, wherein the global predictive model uses a base feature extractor to identify the presence of lung cancer in the data set , and wherein the global predictive model includes a layer with an attention mechanism; b) supplying the data set to a two-stage prediction model, the two-stage prediction model comprising: 1) a first stage detection model detecting the location of one or more three-dimensional cancer candidates within the data set , wherein the first stage detection model uses the base feature extractor, and 2) a second stage probability model operating on the one or more three-dimensional cancer candidates detected by the first stage detection model and assigning a cancer probability p to each of the three-dimensional cancer candidates ; and 1) a first stage detection model detecting the location of one or more three-dimensional cancer candidates within the data set , wherein the first stage detection model uses the base feature extractor, and 2) a second stage probability model operating on the one or more three-dimensional cancer candidates detected by the first stage detection model and assigning a cancer probability p to each of the three-dimensional cancer candidates ; and c) generating data representing an overall prediction of the probability of cancer in the data set using both the prediction of the global predictive model and the cancer probabilities p assigned by the second stage probability model to each of the three-dimensional cancer candidates , and the location of cancer in the data set , wherein the location of cancer is determined by either the global predictive model or the two-stage prediction model. <EOS>