---
# System prepended metadata

title: SIIM-FISABIO-RSNA COVID-19 Detection
tags: [object detection, chestxray, image classification challenge]

---

# SIIM-FISABIO-RSNA COVID-19 Detection

###### tags: `chestxray`, `image classification challenge`, `object detection`

**Table of contents**
[TOC]

## Step 1: modeling

- [x] Problem setup
- [x] Algorithm Design
    - [ ] multi-task learning.
    - [x] classification in tandem with object detection (**current**)
    - [ ] design segmentation algorithm.
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#### :bulb: **TRAINING STRATEGY 1: teacher-student network (2-3 stages)** (FOR CLASSIFICATION ONLY).
:rocket: **Insights**: 

1) human input supervision by color.
2) teacher convert supervision to feature map.
3) student must find real signals to produce the same feature map as the teacher. (i.e. find the image features that is equivalent as if the image are colored)

:arrow_right: the role of the teacher is to convert human supervision to feature map supervision.

- annotated images: normal image with **BOUNDING BOXES OR GROUND TRUTH ATTENTION.**

![](https://i.imgur.com/X2HNjOM.png)





#### :bulb: **TRAINING STRATEGY 2: multi-task learning model**

- YoloV4
- YoloV5
- scale-YoloV4
- ==YOLOR==

#### :bulb: **TRAINING STRATEGY 3: LSTM + CNN to predict all images for a study together**.



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## Step 2: data exploitation (scaling up with more data)
- [x] domain knowledge
- [ ] scaling up with more data
- [ ] take advantage of annotations.

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s


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## Step 3: data explosion --> post-processing.

- [ ] dark magic (CMPM tricks): ** 0.5 / nums_model (need statistical proof).
- [ ] rank average ensemble.
- [ ] correlation between 'negative' and 'no-opacity'.
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## Appendix


