Medical Imaging

MICCAI 2026: At the time of writing, MICCAI 2026’s paper reviews have not been released yet. Based on the program schedule and author emails, 1,166 papers were accepted from 4,601 submissions (25.36% acceptance rate). There will be 99 oral and 54 spotlight talks. The early acceptance rate was 9% (of the total number of submissions) this year. Based only on the information in the program schedule, I have put together some visualizations for this year. I will update the website with the complete analysis when the reviews are released. ...

Note. This tutorial's interactive widgets and its citation previews require JavaScript to be enabled in your browser. If you were to ask seven radiologists to outline the same structure on the same medical image, it is almost a certainty that you will get seven different masks. To someone unfamiliar with medical imaging, this might seem like carelessness, but it is not. Boundaries in medical images are genuinely ambiguous, and if you add personal annotation preferences or “style”, expertise levels, and other factors, the disagreement starts to make sense. Across organs and modalities, the volume of a manually contoured structure routinely varies by tens of percent between observers (for example, inter-observer volume differences of 20% and more are common in CT organ delineation), and that variation propagates directly into downstream tasks: radiotherapy decisions, measurements, and the “ground truth” on which segmentation models are trained and evaluated. When these annotations feed a supervised model, the choice of how to collapse them into a single training target is a modeling decision, and this tutorial shows how different methods can make that decision differently. ...

nnU-Net is now considered a standard and state-of-the-art tool for medical image segmentation, but, I think that it is opinionated in the following ways that affect how I may use it: It enforces a specific training and evaluation workflow, including assumptions about data splits. This means using custom train-valid-test splits requires some workarounds. It assumes 3D volumetric data, and while 2D data is supported, it’s the not the primary use case. It assumes grayscale images, and does not support RGB images out of the box. This again means that RGB images require some workarounds. A lot of researchers, however, happily use nnU-Net for 3D volumetric medical images, so these limitations may not be relevant to them. ...

The paper proposes using Generative Adversarial Networks (GANs) to augment the dataset with high quality synthetic liver lesion images in order to improve the CNN classification performance for medical image classification. The authors use limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The liver lesions vary considerably in shape, contrast and size, and also present intra-class variability. ...