Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as \textit{PolypGen}) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation. Our dataset can be downloaded from \url{ https://doi.org/10.7303/syn26376615}.

University at Buffalo computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes. The tool proved 94% effective with portrait-like photos in experiments described in a paper accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing to be held in June in Toronto, Canada. "The cornea is almost like a perfect semisphere and is very reflective," says the paper's lead author, Siwei Lyu, Ph.D., SUNY Empire Innovation Professor in the Department of Computer Science and Engineering. "So, anything that is coming to the eye with a light emitting from those sources will have an image on the cornea. "The two eyes should have very similar reflective patterns because they're seeing the same thing.

Computer scientists have developed a tool that detects deepfake photos with near-perfect accuracy. The system, which analyzes light reflections in a subject's eyes, proved 94 percent effective in experiments. In real portraits, the light reflected in our eyes is generally in the same shape and color, because both eyes are looking at the same thing. Since deepfakes are composites made from many different photos, most omit this crucial detail. Deepfakes became a particular concern during the 2020 US presidential election, raising concerns they'd be use to discredit candidates and spread disinformation.

Question: Which of these people are fake? University at Buffalo deepfake spotting tool proves 94% effective with portrait-like photos, according to study. University at Buffalo computer scientists have developed a tool that automatically identifies deepfake photos by analyzing light reflections in the eyes. The tool proved 94% effective with portrait-like photos in experiments described in a paper accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing to be held in June in Toronto, Canada. "The cornea is almost like a perfect semisphere and is very reflective," says the paper's lead author, Siwei Lyu, PhD, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering.