Robo-Semantic-Segmentation VS medicaldetectiontoolkit

Hi, you can look at this https://github.com/The-ML-Hero/Robo-Semantic-Segmentation/ which is my GitHub repo. This repo is all about segmentation specifically semantic segmentation, I have a couple of questions where did you get the dataset? and do you have the dataset ready?. But before you use the code be sure to understand the workings of semantic image segmentation architectures. The repo is implemented in Pytorch which is in the python language.

Checkout https://github.com/MIC-DKFZ/medicaldetectiontoolkit, they have a recent 3d rcnn implementation. Their paper is a good start on the topic. Also have a look at nnDetection from the same group, it might provide some more leads. Hth

I'm doing research in the area of biomedical image processing using deep learning. At the moment, I'm focused in the detection of biological structures in 3D microscopy images. For this task I trained a Faster R-CNN architecture, however I noticed that while the training loss is decreasing the validation loss is high, doesn't decrease and sometimes oscillates. When I looked at the results, the bounding boxes in the training and validation sets were too small but located at random spots. At first I thought that I should adjust the size of the anchors, however, even after trying different anchor sizes the results didn't improve. I would like to know if anyone has experience in working with 3D Faster R-CNN that could help me with suggestions for this project. (btw, I'm using the code available in this repo). Thanks in advance.