Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.

We introduce a weakly supervised segmentation approach that leverages class activation maps and the Segment Anything Model to generate high-quality masks using only classification data. A pre-trained classifier produces class activation maps that, once thresholded, yield bounding boxes encapsulating the regions of interest. These boxes prompt the SAM to generate detailed segmentation masks, which are then refined by selecting the best overlap with automatically generated masks from the foundational model using the intersection over union metric. In a polyp segmentation case study, our approach outperforms existing zero-shot and weakly supervised methods, achieving a mean intersection over union of 0.63. This method offers an efficient and general solution for image segmentation tasks where segmentation data are scarce.

Map2VidSeg uses classifier-derived localization maps to generate prompts for video segmentation without segmentation training data. We revisit the pipeline under a fixed 224 × 224 visual-prompt regime and test whether newer foundation-model components improve performance. Starting from the CBMS 2025 system of DINOv2 attention, YOLOE, and SAM-2 on SUN-SEG polyp video, we replace the components along three axes: DINOv3 for map generation, YOLOE-26, for prompt-based segmentation, and SAM-3 for temporal refinement. A completed 2 × 2 prompt-source × YOLOE-stage factorial with SAM-3 fixed shows that the full DINOv3 + YOLOE-26 + SAM-3 upgrade reaches 0.6040 IoU and 0.6684 Dice on SUN-SEG Unseen, whereas the DINOv2 + YOLOE-11 + SAM-3 control reaches 0.6750 IoU and 0.7426 Dice. The prompt-source swap is near neutral with YOLOE-11, whereas YOLOE-26 is weaker at this prompt resolution and the full upgrade underperforms the additive expectation. In a matched comparison using identical cached YOLOE masks, SAM-3 tracker double-pass produced a modest paired gain over SAM-2 double-pass (＋0.0275 IoU; 95% BCa CI [+0.0122,+0.0458]; p=0.0012). A second-task diagnostic study on CholecSeg8k binary instruments shows the same pattern: integration changes improve the YOLOE stage, whereas SAM-3 reduced performance on multi-tool clips under shared-identity tracking, and class-aware tracking eliminated this loss. The results support pipeline-level evaluation and identify class factoring as a third design axis alongside prompt quality and prompt-model compatibility.