ENABLING 3D SEGMENTATION FOR DETECTING KIDNEY TUMORS

One of the common tasks on these datasets is to segment regions of interest such as tumors/cysts on specific organs. If these masses are small enough, then using a lower resolution image can lead a model to ignore those sections. A large number of researchers work on models that can accurately segment out tumors in these medical scans. The biggest constraint for these kinds of models and high resolution datasets is memory resources.

Most architectures that support machine learning applications run out of memory due to the large size of intermediate activation maps in the network. Using data or model parallelism does not solve this problem. Therefore, the most common alternative used in these cases is downsampling the volumes to a lower voxel space or training models on smaller crops of the volume. This can lead to a loss of contextual cues and therefore suboptimal performance on the dataset.

We hypothesize that training models on the true resolution of the dataset will help achieve better accuracy.